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Purpose The purpose of this paper is to provide comprehensive information on preventive maintenance (PM) planning and methods used in the industry in order to achieve an effective maintenance system. Design/methodology/approach The literature review is organized in a way that provides the general overview of the researches done in the PM. This paper discusses the literatures that had been reviewed on four main topics, which are the holistic view of maintenance policies, PM planning, PM planning concept and PM planning-based in developing optimal planning in executing PM actions. Findings PM policy is one of the original proactive techniques that has been used since the start of researches on maintenance system. Review of the methods presented in this paper shows that most researches analyse effectiveness using artificial intelligence, simulation, mathematical formulation, matrix formation, critical analysis and multi-criteria method. While in practice, PM activities were either planned based on cost, time or failure. Research trends on planning and methods for PM show that the variation of approaches used over the year from early 1990s until today. Practical implications Research about PM is known to be extensively conducted and majority of companies applied the policy in their production line. However, most analysis and method suggested in published literatures were done based on mathematical computation rather than focussing on solution to real problems in the industry. This normally would lead to the problems in understanding by the practitioner. Therefore, this paper presented researches on PM planning and suggested on the methods that are practical, simple and effective for application in the real industry. Originality/value The originality of this paper comes from its detail analysis of PM planning in term of its research focus and also direction for application. Extensive reviews on the methods adopted in relation to PM planning based on the planning-based such as cost-based, time-based and failure-based were also provided.
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Journal of Quality in Maintenance Engineering
Preventive Maintenance (PM) planning: a review
Ernnie Illyani Basri Izatul Hamimi Abdul Razak Hasnida Ab-Samat Shahrul Kamaruddin
Article information:
To cite this document:
Ernnie Illyani Basri Izatul Hamimi Abdul Razak Hasnida Ab-Samat Shahrul Kamaruddin , (2017)," Preventive Maintenance
(PM) planning: a review ", Journal of Quality in Maintenance Engineering, Vol. 23 Iss 2 pp. -
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http://dx.doi.org/10.1108/JQME-04-2016-0014
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Preventive Maintenance (PM) planning: a review
Abstract
Purpose – The purpose of this review paper is to provide comprehensive information about
preventive maintenance (PM) planning and methods used in industry to achieve an effective
maintenance system.
Design/Methodology/Approach – The literature review is organized in a way that provides a
general overview of the research conducted concerning PM. This paper discusses the
literature that has been reviewed in terms of four main topics, which are the holistic view of
maintenance policies, PM planning, PM planning concepts and PM planning based on
developing optimal planning when executing PM actions.
Findings PM policy is one of the original proactive techniques that has been used since
research into maintenance systems began. A review of methods presented in this paper shows
that most research has analysed effectiveness using artificial intelligence (AI), simulation,
mathematical formulation, matrix formation, critical analysis and multicriteria methodology.
While, in practice, PM activities tend to be planned based on cost, time or failure, research
trends on planning and methods for PM show that there have been variations of approaches
used over the years from the early ‘90s to the present day.
Practical implications Research about PM is known to have been conducted extensively
and the majority of companies have applied the policy in their production line processes.
However, most of the analyses and methods suggested in the published literature were based
on mathematical computation rather than on solutions derived from real problems
experienced by industries. Normally, this would lead to problems in understanding by
practitioners. Therefore, this paper presents research on PM planning and suggests methods
for application in real industrial situations that are practical, simple and effective.
Originality/Value The originality of this paper comes from its detailed analysis of PM
planning in terms of its research focus and also the direction for its application. Extensive
reviews of the methods adopted in relation to PM planning, such as cost-based, time-based
and failure-based planning, have also been provided.
Keywords – Maintenance policy; preventive maintenance; planning, cost-based; time-based;
failure-based
Article classification – Literature review
1. Introduction
In general, maintenance is defined as the combination of all technical and administrative
actions, including supervision, which ensure that a system is in its required functioning state
(Reason, 2000; Swanson, 2001). Maintaining a system is usually related to maintenance
actions such as repairing, replacing, overhauling, inspecting, servicing, adjusting, testing,
measuring and detecting faults in order to avoid any failure that would lead to interruptions in
production operations (Duffuaa et al., 2001; Ismail et al., 2009). Performance measurement
for maintenance systems can be based on various factors (Parida et al., 2015).
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According to Wikstan and Jonannson (2006), effective maintenance can reduce the
consequences of failure and extend the life of a system. Implementation of maintenance
refers to maintenance policies, which can be defined as the plans of action used to provide
direction and guidelines to carry out further maintenance actions required by a system
(Waeyenbergh and Pintelon, 2002). Corrective maintenance (CM) is one of the maintenance
policies by which maintenance actions, such as repair or replacement are carried out on a
system to restore it to its required functioning after it has failed (Paz and Leigh, 1994).
However, this policy leads to high levels of system breakdown and high repair and
replacement costs, due to sudden failures that potentially can occur. Another maintenance
policy, PM, serves as an alternative to CM. Normally, PM is planned and performed after a
specified period of time, or when a specified system has been used, in order to reduce the
probability of its failure (Kimura, 1997). Mechefske and Wang (2001) claimed that most
systems are maintained whilst a significant amount of their useful life remains whenever PM
practices are applied.
In this paper, a review has been carried out based on the challenges faced during PM
planning processes in order to secure better rates of improvement for organizations. The
review has been structured in a way that provides a general overview of studies related to PM
as well as PM planning, followed by an in-depth discussion of PM planning concepts. The
three categories of PM planning cost-, time- and failure-based have been reviewed
thoroughly. Finally, an analysis of trends in published research concerning PM planning has
been summarized, before closing with suggestions for potential future research directions.
At the end of this paper, it is hoped that people at all levels in maintenance systems
may have benefited from considering this review. Most directly, academicians can use this
paper as a guideline for understanding the principles and methods underpinning PM, based on
the literature presented. For this research, the reviews of methods and tools also provide
practical insights for managers. “Management” refers to the process of leading and directing
companies by deploying resources, which involves shouldering the responsibilities for
making technical and administrative changes to production and maintenance processes and to
top management personnel themselves (Murthy et al., 2002).
The expected outcome from management’s point of view can be discussed in terms of
two levels of management activity, i.e. at the strategic and tactical levels. At the strategic
level, which corresponds to Al-Turki’s (2011) work, a business’s priority is to address
generic PM planning as a key supporting function, where the aspects of planning should meet
the requirements of a business. At the tactical level, maintenance priorities should support
businesses at the strategic level and at the operational level when maintenance is undertaken,
and those priorities should fulfil the requirements of PM planning. This corresponds to
Márquez’s (2007) idea of how standard process planning refers to the assignment of
maintenance resources, such as manpower, spare parts and tools as part of detailed planning
and scheduling, to ensure that the execution of maintenance at the operational level is
acceptable and practicable. This detailed and structured review of PM should help to ease the
planning and scheduling of maintenance systems for practitioners and also for maintenance
service providers.
2. Overview of maintenance policies
Nowadays, the efficiencies and effectiveness of the whole of a manufacturing operation
are dependent on the sustainable performance of systems or equipment, which can lead to
valuable improvements in terms of quality, cost and time (Nakajima, 1986; Khan and Darrab,
2010). In order to produce better product quality at a minimal cost, the availability and
reliability of the production line, widely known as “the system”, plays a central role in
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sustaining a competitive edge over other manufacturing organizations (Muchiri et al., 2011).
The word “system” in engineering terms is translated as, “the assemblage of machines which
consists of mechanical or electrical devices that transmits energy to assist in the performance
of human tasks”. “System” usually refers to the machines by which equipment, such as tools,
as well as materials, people and information, are utilized to produce value-added physical and
informational products. Hence, systems are considered to be an inevitable part of production,
which require constant attention and maintenance to achieve the desired operating conditions
(Ahmed et al., 2005).
Unfortunately, a system is always subject to deterioration in the course of continuous
operations. The function of a system will change over time as the importance of maintenance
to the system increases due to technical developments, the changing of laws and regulations
and variances in operational environments (Söderholm et al., 2007). Moreover, the
complexity of a system is a crucial component of the critical requirements of safety and costs
throughout its life cycle (Liyange and Kumar, 2003; Foley, 2005). Hence, maintaining a
system is extremely important as it requires a proper and effective maintenance policy to
ensure that there is a capability for the system to perform its required functions. Table 1
illustrates threetypes of maintenance policies that are normally adopted by industries. The
policies come with many features that suit different situations and implementation stages.
The basic objectives of maintenance policies are to reduce unplanned system breakdowns and
to increase available operational time.
Table 1: Maintenance policies (Source: Parajapathi et al., 2012)
Features
Maintenance policies
Corrective
Maintenance
(CM)
Preventive
Maintenance
(PM)
Predictive Maintenance (PdM)
Maintenance
approach
Reactive Proactive Proactive
Maintenance
category Fixing after
failure
Time-based
maintenance
(periodic)
Diagnostic-based
maintenance
(condition
monitoring)
Prognostic-
based
maintenance
(reliability-
centred)
Downtime Highest Less Close to minimum
Least
Good for
failures
Random age-
based Age-based
Prevents to occur
(near-optimal)
Prevents to
occur
Expensive
(manpower)
Maximum Little less Moderate Minimum
Initial
None
Slightly higher
Expensive
Most
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deployment
cost
expensive
Computational
cost
Least Little higher Higher Highest
Schedule
required Not
applicable
Based on the
standard useful
life of component
or history of
failures
Based on current
conditions
Based on
forecast of
remaining
equipment life
Action
Inspect,
repair or
replace after
failure
Inspect, repair or
replace at
predetermined
intervals,
forecasted by
design and
updated through
experience
Inspect, repair or
replace based on
need.
Continuous
collection of
condition-
monitoring data
Forecasting of
remaining
equipment life
based on
actual stress
loading
Prediction type None None
On and off system,
near-real-time trend
analysis
On and off
system, real-
time trend
analysis
Preventive maintenance (PM) was introduced in the 1950s, after the recognition of the
need to prevent failure (Murthy et al., 2002). As an alternative to corrective maintenance
(CM), PM has been adopted for emerging technologies since such systems are generally more
complex than those based on the use of hand tools. The basic principle of a PM system is that
it involves predetermined maintenance tasks that are derived from machine or equipment
functionalities and component lifetimes. Accordingly, tasks are planned to change
components before they fail and are scheduled during machine stoppages or shutdowns.
Meanwhile, predictive maintenance (PdM) is an advancement on PM and commonly involves
condition-monitoring systems. In PdM, repetitive or high-risk failures are studied using
historical data detailing occurrences of a machine’s operational failures and then maintenance
is conducted during its operation, based on the condition of the monitored component. In
summary, PM and PdM are both proactive maintenance approaches and have similar
objectives, but PM is conducted when a machine is stopped, while PdM is undertaken while a
machine continues in operation.
According to Simões et al. (2011), PM is realised from two perspectives, which are
known as the managerial and the operational. The managerial perspective refers to the
support for decision-making which facilitates the analysis of data (Söderholm et al., 2007).
Inputs for the managerial perspective include the determination of PM’s objectives, planning
to perform maintenance actions, and methods involved in solving any problem that occurs
with regard to PM as well as the performance of systems. The managerial perspective is also
known as an outer process, as it bases decisions on history and analysis prior to the execution
of PM actions. Meanwhile, the operational perspective refers to the execution of maintenance
actions in order to sustain the capability of a system to perform its intended functions
(Bjorklund et al., 2010). This perspective is an inner process that consists of technical aspects
by which PM is carried out based on inputs to the outer process.
Both perspectives that prefigure PM are crucial for ensuring its effectiveness and
efficiency. However, the managerial perspective plays the more important role in planning
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and determining suitable and feasible solutions before carrying out PM so that it meets its
objectives. This is because without proper planning, the execution of PM actions could affect
the system or other systems that may then require further planning actions (Ab-Samat et al.,
2012). Therefore, most attention should be given to planning as the key to connecting the
managerial and operational perspectives. With the aid of planning, PM can be directed in a
structured and systematic way to monitor and increase the lifetime of a system.
3. PM Planning
In the context of maintenance, planning encompasses activities that are undertaken with
the aid of all maintenance resources such as material requirements, labour requirements, time
assignments and technical references related to equipment, that are determined and prepared
prior to a task’s performance (Duffuaa et al., 1999). In other words, without proper planning,
inconsistent and unreliable procedures will result, which may lead to interruptions in
production. Therefore, proper planning is basically the preparation for performing necessary
maintenance tasks on a priority basis by referring to the required resources, information and
schedule.
As PM is one of a number of maintenance policies, it is pertinent to that maintenance
planning which requires a long-term strategy for executing maintenance actions within a
predetermined interval. This ensures that a system continues to fulfil its intended function
(Palma et al., 2010). The scope of PM planning covers all the aspects of PM that are to be
integrated with planning in order to aid decision-making, in the cases of actions to be taken
and the performance of the system to be monitored and improved. PM planning is also a
feature of the managerial perspective which requires objectives, planning and methods to be
considered prior to the execution of PM on a system (Basri et al., 2014). From the managerial
perspective, the process of developing PM planning necessitates the incorporation of both
PM policy and planning to ensure that the PM actions are performed in a proven and
standardized way. The significance of having proper objectives, planning and methods is to
provide a better understanding and proper guidelines to facilitate the process of developing
and improving PM planning.
The literature on PM planning concentrates on various aspects in relation to the
maintenance environment. In this research, the focus of PM planning has been narrowed
down and placed on the concept underlying PM planning, which embraces the objectives
behind PM’s performance, the state of the systems involved, as well as the methods applied
to solve maintenance issues. The PM planning concept will be explained in the next section.
4. PM Planning Concept
In general, the PM planning concept is briefly described as the general idea that covers
the elements of PM in a simple and systematic way. The process for determining a PM
planning concept is based on an investigation of the characteristics of the conceptual
description, specifications and application domains (Young, 2003). Hence, in this research,
the literature on the PM planning concept that is reviewed and discussed consists of three
facets:
i. the objective(s) or the purpose of performing PM planning;
ii. descriptions of the system’s state in terms of its importance and its functions; and
iii. methods that are divided into several classifications which help in determining the
best solutions for the issues highlighted.
These facets are important as they provide a guide on how the literature and its substance are
reviewed. Each of the facets of PM planning will be explained further in the subsections that
follow.
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4.1 Purpose(s)
As part of the planning concept, “purpose” is described as the objective or goal
intended by performing PM planning. The reason for establishing significant objectives prior
to the execution of PM planning is to narrow down and focus the study and to guide the
collection of related information. Significantly, PM planning is described as an aid to
decision-making to determine any action that is to be taken based on the outcome or objective
to be achieved from the planning conducted in relation to the issues experienced in the
maintenance environment.
From the aforementioned operational perspective, in which PM consists of technical
operations, the execution of PM is frequently associated with a wide range of issues. These
issues are regarded as technical problems taking place on the production floor that would
affect production processes and the maintenance of quality. Several issues have been
highlighted in terms of PM and its related systems, such as system downtime, system
deterioration, imperfect PM actions, improper time estimation for PM, insufficient numbers
of maintenance personnel and system unavailability (Reason, 2000; Swanson, 2001). Hence,
the issues that have been highlighted have drawn the attention of researchers wishing to solve
them by establishing objectives to be achieved before conducting any actual problem-solving.
For instance, one of the major issues discussed with regard to the maintenance
environment is that of system breakdown, which may lead to other problems such as system
or component deterioration, unplanned failures and interruptions in production processes.
These might be caused by improper planning before undertaking PM actions and the
improper conduct of PM on a system (Percy and Kobbacy, 2000; Liyange and Kumar, 2003).
Therefore, most researchers have studied the issue of minimizing system downtime,
particularly system reliability and availability, setup times, product quality, spare parts and
the complexity of PM actions. Here, PM planning plays an important role by establishing
objectives or purposes in order to solve issues regarding system breakdown. Thus, in the
extant literature some purposes that are commonly discussed are optimal PM intervals, proper
job scheduling and the assignment of PM tasks. The descriptions of these purposes are
presented in Table 2.
Table 2: Description of the purpose
Purpose
Description
s
The time interval
s
for PM actions such as replacement,
which predetermines when to replace a system or
component before they fail.
Proper job scheduling The job-to-job sequencing of PM actions within the range of
a time interval (Cassady and Kutanoglu, 2005).
Assignment of PM tasks
What
PM actions
to undertake
and how to perform
them.
Referring to Table 2, the objective is to set out the intentions for studying PM planning
as derived from literature reviews, since doing so will guide PM planning reviewers in a
highly systematic and organized manner. Besides setting out the purposes behind PM
planning, another important element in explaining the PM planning concept is to describe the
state of systems, which will be explained further in the subsections that follow.
4.2 The state of systems
Other than defining objectives, the PM planning concept encompasses the description
of a system’s state, its function and importance. A system is envisaged as an assembly of
interconnected components arranged to carry out a process. From an operational perspective,
the system is defined in terms of its state, which refers to its condition with respect to its
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attributes. The purpose of considering the state of a system is to represent it as operating
normally, as operating in breakdown mode or as having failed completely. Thus, the
decision-making for PM planning is based on the state of the system, which seeks to base an
analysis on the condition of the system with respect to its function. Therefore, most
researchers have concentrated their work on solving maintenance problems by paying
attention to the state of systems prior to further measurement and analysis. This is where PM
planning should consider the system’s state to assist with further complex analysis. In the
literature, the state of a system is categorized in two ways, i.e. as single-unit and multi-unit
systems. A description of the state of systems in PM planning is presented in Table 3 below.
Table 3: Description of the state of systems (Source: Cho and Parlar, 1991)
System’s state
Component’s state
System configuration
Single system Single component No
Multiple components Serial or parallel
Multiple systems
(multi-systems)
Multiple components
Serial or parallel
Referring to Table 3, a single-unit system consists of either one component or multiple
components. By contrast, multiple or multi-unit systems consist of several system units with
several components. Furthermore, the elements of each system state are arranged
mechanically into two configurations, namely the serial and the parallel. In a serial
configuration, the entire system fails if any one of the system’s components fails. By contrast,
for a parallel configuration, the entire system works as long as not all the systems or
components fail. Hence, if any problem occurs in any one system, the other systems or
components may also be affected (Cho and Parlar, 1991; Khanlari et al., 2008). Without a
proper maintenance system, a maintenance engineer might replace the entirety of a system’s
equipment on its failure, which would be very costly (Rana, 2014).
By representing a system in terms of its state, PM can be conducted with the aid of
methods applied for solving the issues that have caused a system’s breakdown. As a system
becomes more complicated, more sophisticated PM planning is needed to solve the
maintenance issues related to the system’s performance. Therefore, suitable methods that can
deal with maintenance issues dynamically should be considered as part of the development
process for optimal PM planning. Thus, the state of systems has been a focus of the review of
the literature concerning PM planning and proper attention has been given to the condition of
systems. Besides considering the state of systems, the other important element in explaining
the PM planning concept is the methods used, which will be explained in subsequent
subsections.
4.3 Methods
Another important element of the PM planning concept is the methods applied to find
the best solution for maintenance issues raised in relation to PM planning. The method is the
description of any particular procedure followed to determine an optimal PM plan under
certain maintenance requirements or constraints. The purpose is to assist with related analysis
through an established and systematic procedure, which can facilitate the achievement of
certain accurate and efficient results. Thus, the decision-making in PM planning is based on
the methods applied, which can be affected by the outcomes or results of analysis. Therefore,
most researchers have studied the most suitable and applicable methods for solving
maintenance problems, particularly when considering the measurement and analysis steps. In
the literature, several methods have been applied by researchers regarding PM planning,
which are classified into six categories as shown in Table 4 below.
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Table 4: Descriptions of methods applied in PM planning
Methods
Classifications
Descriptions
Fuzzy logic,
g
enetic
algorithm (GA), neural
network, Markov chain,
Bayesian network, heuristic
algorithm.
Artificial intelligence
(AI)
The theory and development of
computer systems which are able to
perform tasks and to allow systems to
perform functions that would
normally require human intelligence
(Kobbacy, 2008).
SIMAN, Monte Carlo,
Witness.
Simulation A computable technique which has
the capability to analyze, design and
operate complex systems for better
understanding without affecting the
real system (Alabdulkarim et al.,
2013).
Linear programming,
n
on
-
linear programming, integer
linear programming (ILP),
dynamic programming,
mixed integer linear
programming (MILP),
Weibull distribution,
proportional hazard model
(PHM).
Mathematical
formulation
A mathematical representation
for
making a decision on the best
possible allocation of scarce
resources (Rommelfanger, 1996).
Similarity
c
oefficient
m
atrix
.
Matrix formation
A rectangular array that encompasses
numbers, symbols, or other
mathematical objects for which
operations like addition and
multiplication are defined
(Romesburg, 2004).
Failure mode and effect
analysis (FMEA), failure
mode effect and critical
analysis (FMECA).
Critical analysis Detailed studies in identifying the
failure mechanism and criticality of
the failures that occur and that would
affect the condition and the lifetime
of a system (Zhao, 2003).
PROMETHEE,
a
nalytic
network process (ANP),
analytic hierarchical process
(AHP).
Multi
-
criteria
Decision method that uses various
alternatives by considering all
conflicting criteria and the judgments
of a decision-maker (Labib et al.,
1998).
Based on the three elements of the PM planning concept, the reviews are structured in a
way that represents planning in conjunction with the objectives, the state of systems and the
methods applied. Therefore, it is important to have the basics of the planning outlined for
maintenance issues and that process is familiarly known as planning-based PM. Planning-
based PM is perceived as a fundamental analysis that should be undertaken before any
planning is conducted, in accordance with the elements of the PM planning concept.
5. Planning-based PM
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Planning-based PM is all about the essential analysis of comprehensive planning for
PM. It involves the fundamental maintenance criteria of systems as the basis for an analysis
that derives the best PM plan. Planning-based PM is categorized in three ways, i.e. cost-
based, time-based and failure-based planning. Details of planning-based PM will be
discussed in subsequent subsections.
5.1 Cost-based
Cost-based planning analyzes the capital cost and benefits to organizations of PM, as
well as the revenue it helps to generate (Turkcan et al., 2007). It is important to have cost-
based planning as a fundamental assessment as it compares the costs of solutions with the
economic benefits that would be gained if the solution was put into effect. In the PM
planning literature, cost-based planning analysis makes reference to the cost involved when
evaluating maintenance factors, such as the costs for repair, replacement, spare parts, tools
and manpower. According to Stenstrom et al. (2016), maintenance costs are formulated
dependent on the cost of downtime, reliability characteristics and the redundancy of assets.
The methods are conducted based on the maintenance factors that affect PM planning’s
effectiveness.
Kobbacy et al. (1997a) have performed cost-based planning in order to determine the
relationship between the PM interval and the operating cost per unit of time, and the
component availability of a single-unit system. The authors used the Weibull distribution
method to determine the significance of correct failure time and to compare the effect of the
distribution, whether exponential, Weibull, normal, log normal or gamma, with PM intervals.
Furthermore, Kardon and Fredendall (2002) developed a mathematical formulation using
Weibull distribution for three types of system states, i.e. single-unit systems with a single
component, single-unit systems with multiple components and multi-unit systems with
multiple components, which incorporated the overall probability breakdown and the cost of
PM decisions for multi-unit systems in serial configurations. Based on the comparison
between all the system states, the outcome of the analysis offered an optimal PM interval
based on the minimum total expected costs that eased the decision to perform PM.
Mijailovic (2003) compared Weibull and exponential distributions in probability
calculations of maintenance costs throughout the PM planning process. The calculation was
applied to a single-unit system and multiple components and took two parameters into
account, which were the period between PM scheduling and the availability of components.
Based on the comparative costs, an optimal PM interval which minimized the cost per unit of
time, or maximized the component availability was selected. In another study, Bartholomew-
Biggs et al. (2006) investigated PM scheduling for single systems which reflected the cost of
performing PM. The authors used Weibull distribution with hazard rate models to predict the
frequency of a system’s failure in terms of cost optimization. Based on the complexity of the
mathematical formulation in the analysis, an optimal PM interval was determined by
calculating the optimum mean cost for performing PM. When set the main objective of
minimizing total costs, Nourelfath et al. (2016) developed an optimization model using
Weibull distribution and a solution algorithm that integrated quality, production and
maintenance parameters for an imperfect process in a multiperiod, multiproduct, capacitated,
lot-sizing context. The authors also proved that an increase in PM levels led to reductions in
quality control costs when the proposed model was adopted.
Other mathematical formulation methods are dynamic programming, integer
programming and mixed integer linear programming (MILP). Vaughan (2005) addressed the
inventory of spare parts by grouping identical components that proved to be economical for
PM. The author had performed the analysis in a single-unit system of multi components in
order to determine the optimal PM interval. Das et al. (2007) proposed a PM planning model
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for performance improvement in terms of cost-effectiveness on a cellular manufacturing
system. By incorporating integer programming, the authors compared the model in two
scenarios; with and without considering PM. Two maintenance criteria were considered,
namely a system’s reliability and resource utilization, to determine the optimum time interval
for PM by minimizing the failure repair cost of the system as well as PM costs. Furthermore,
the authors grouped the systems according to the optimum PM interval to improve the
reliability and utilization of the system. Hence, by comparing the two scenarios, the scenario
which considered PM provided a significant improvement in terms of system reliability and
total cost reductions, and thus achieved the objective of PM planning.
In their study, Goel et al. (2003) presented a MILP for integrated design, production
and PM planning. The authors applied the method in a multi-process plant environment. At
the design stage, a reliability allocation model was combined with the existing optimization
framework to determine the initial reliability of each unit of the component and its optimal
size. Hence, at this stage, the operational availability of the proposed integrated approach was
improved. The outcome of PM planning was presented in terms of an optimal schedule for
each component within a predetermined time interval based on the analysis of the
maintenance costs and component availability.
Meanwhile, Wu and Zuo (2010) investigated the relationship between linear, non-linear
and hybrid programming models on a single-unit system. Three maintenance factors were
involved in the analysis, which were the failure rate, the system cost and its reliability. The
expected cost of the three programming models for the sequential PM were formulated, and
necessary conditions for determining the optimal PM policies for both linear and non-linear,
as well as hybrid cases, were derived. Integrating quality improvements into PM decision-
making, Lu et al. (2016) proposed a joint model for a deteriorating single-machine
manufacturing system. In this instance, machine reliability was developed based on a
proportional hazard model (PHM) considering the influences of the degradation states of
quality-related components on machine reliability. Validated by a case study, the
effectiveness of the model was proven when an optimal PM schedule was obtained. Overall
production costs had also been minimized due to reductions in loss of quality as well as repair
cost savings.
Other than as mathematical formulations, AI techniques had also been applied widely
in cost-based planning. Methods that are commonly applied in AI environments are the
genetic algorithm (GA), fuzzy rules and the Markov chain. Lapa et al. (2006) built an
optimization model for PM planning with respect to a single-unit system with multiple
components. Two main criteria had been considered in the developed models which were
applicable to unavailable (undergoing maintenance) active operations and components for
evaluation procedures. The model was verified through the electrical-mechanical system of a
nuclear power plant. The GA was adopted, where the optimal PM that had a high level of
reliability with low cost was the search process of the developed model. The result of
minimizing total PM costs showed the best schedule job for each component based on the
period between the PM schedule and the availability of the system. Bris et al. (2003), on the
other hand, proposed a PM planning model in a multi-unit system that was arranged in series
and parallel configurations to ensure that the assignment of the properly scheduled PM would
be cost-effective. The authors proved that by using the GA in the developmental model, total
costs could be optimized by analyzing the system availability and PM intervals.
Mahadevan et al. (2010) used an advanced method that combined GA and a simulated
annealing heuristic method to optimize PM planning for a single-unit system with multi
components in a process industry. The authors highlighted the objective function for these
analyses, which were the PM time for and costs of replacement, the time to repair, downtime,
failure and standby, which basically constituted the problem of combining the maintenance
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actions for the components during the entire design phase. As a result, the outcome for PM
planning was the assignment of PM tasks, which was based on the critical components for all
systems that provided the minimum cost for the entire design period. Furthermore, Lin and
Wang (2012) established an extension from the study by Bris et al. (2003), which presented a
hybrid GA to optimize the periodic PM in a case study conducted for a multi-unit system
arranged in series and parallel configurations. Development of the hybrid GA concerned the
structure of reliability, block diagrams, the maintenance priority of components and a PM
schedule. Instead of applying a conventional trial-and-error process, a response surface
methodology (RSM) was adopted to determine the crossover and mutation probability in the
GA. The outcome from this study was the discovery of the optimal PM interval based on the
minimized total PM cost.
In addition, Khanlari et al. (2008) used fuzzy rules to prioritize the systems for
performing PM. The objective of the planning was to reduce the total costs for maintenance,
and indirectly minimize the system’s breakdown. Planning was conducted in a multi-unit
system and PM was used to measure the reduced product quality or the lost production
capacity. In the developed algorithm, six criteria were identified, which were the sensitivity
of operation, the mean time between failures, the mean time to repair, workload, as well as
the availability of required parts and the availability of repair personnel. However, due to the
limited number of equipment selected in the production plan, a method for maximizing the
system reliability was difficult to achieve. In a study of the state of a single-unit system with
multiple components, Tosun and Kuruüzum (2009) proposed a model for improving the
system’s availability and decreasing the repair costs due to system downtime. The authors
used a Markov chain in AI to represent the sequence values in the system by focusing on the
maximum availability and minimum cost in order to find the optimal inspection period. It
was shown that in the numerical examples, system availability was maximized with the
minimization of the PM’s cost in order to find the optimal PM interval.
Panagiotiduo and Tagaras (2007) also used the Markov chain method to derive the
optimal PM interval in terms of cost for a single-unit system. The analysis was conducted by
considering the condition of quality (such as in and out of control) and failure rate.
Throughout the analysis, the outcome of the cost optimization was the condition of quality
before the planning for the system and PM interval, which was based on shift and failure time
distribution. Also adopting AI, Fitouchi and Mourelfath (2014) proposed an integrated model
for production and general preventive maintenance planning for multi-state systems that was
solved by the exhaustive search (ES) method and the simulated annealing (SA) algorithm via
numerical experimentation. The integrated model simultaneously gave appropriate instants
for preventive maintenance and production planning decisions, which improved the total
production and maintenance costs.
For non-AI cost-based planning, simulation techniques also received great attention in
the literature. Knapp and Mahajan (1998) optimized a manpower model which aimed to
reduce the cost of maintenance resources by optimizing the allocation of the cost of
manpower based on workload demand. The optimization model was implemented using a
simulation analysis (SIMAN). The model provided statistical information, such as the
utilization of workers and the queue lengths of failed systems in each area and for each craft
type. Results showed an improvement of the overall performance due to the allocation of
workers, worker utilization and queue lengths, which helped with decision-making for PM
planning in regard to the workers’ assignments.
Matrix formation was also included in the cost-based planning experiments of which
one, known as the SCM, was used in a similar matrix structure. Talukder and Knapp (2002)
used the group technology (GT) concept of the heuristic method to group the systems into
blocks within a multi-unit system in a series configuration in order to make decisions on the
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block or grouping of the system, which minimized the PM cost. Single linkage clustering
(SLINK) is one examples of SCM which was used with heuristic-based calculations to
generate groupings of similar systems. The cost of the block PM for each group was
calculated using the developed cost model. In the developed block of the PM cost model, the
PM interval was measured by reducing the PM cost for individual systems in groups and the
time interval was analyzed heuristically using the analysis of variance (ANOVA). The
authors stated that the approach generated excellent results for real-size problems, thus it may
be able to assist the planning of PM on systems in groups.
By contrast, examinations of multi criteria methods is very limited in the literature as it
involves solving complex decision problems. AHP is one method that falls under the multi-
criteria umbrella. Labib et al. (1998) used AHP to develop a dynamic and adaptable
procedure in a multi-unit system that utilized the existing data and supported decisions for
planning purposes accordingly. The methodology consisted of three hierarchical stages such
as criteria decision analyses, criteria prioritization and system critical judgments. The
outcome of the methodology helped decision-making processes by prioritizing the system’s
criticality and focusing on specific components for PM operations.
Overall, according to the review, cost-based planning was used aggressively to evaluate
the total anticipated cost and effectively weighed the costs and benefits of the proposed
method with regards to PM planning. However, there were a number of arguments against
cost-based planning as a decision-making tool. The ambiguity and uncertainty involved in
quantifying and assigning a monetary value to intangible items could lead to inaccurate
analysis. Thus, it may increase risks and cause inefficient decision-making. A summary of the
review is presented in Table 5.
Table 5: Summary of cost-based in PM planning
Category
Method
System’s state
Outcome
References
Mathematical
formulation
Weibull, normal
and gamma
distribution
Single-unit
system with
single
component
Optimal PM
interval
Kobbacy et al.
(1997)
Dynamic
programming
Single
-
unit
system and
multi-unit
component
Optimal PM
interval
Vaughan (2005)
Probability and
reliability
function
Multi
-
component
system
Cost rate and
PM with
warranty period
Darghouth et al.
(2016)
Mathematical
formulation
Weibull and
exponential
distribution
Single-unit
system
An optimal PM
interval which
minimized cost
per unit time
Mijailovic
(2003)
Weibull and
exponential
distribution
Single
-
unit
system
An optimal PM
interval which
minimized cost
per unit time
Mijailovic
(2003)
Weibull
distribution
Single-unit
system
Optimal PM
interval
Bartholomew-
Biggs et al.
(2006)
Weibull
distribution and
Multi
-
unit
system
Improvement of
total cost
Nourelfath et al.
(2016)
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algorithm
Dynamic
programming
Single-unit
system and
multi-unit
component
Optimal PM
interval
Vaughan (2005)
Integer
programming
Multi
-
unit
system
Optimal PM
interval in a
group of
systems
Das et al. (2007)
MILP
Multi
-
unit
system
Proper PM
scheduling job
Goel et al.
(2003)
Linear,
nonlinear,
hybrid
Single
-
unit
system
Optimal PM
scheduling job
Wu and Zuo
(2010)
Proportional
hazard model
(PHM)
Single-unit
system
Optimal PM
scheduling job
Lu et al. (2016)
Artificial
intelligence (AI)
GA
Single
-
unit
system and
multi-
component
Proper PM
scheduling job
Lapa et al.
(2006)
Monte Carlo
technique/GA
Multi
-
unit
system (series
and parallel)
Optimal PM
interval
Bris et al.
(2003)
GA and
simulated
annealing
Single-unit
system and
multi
component
Assignment of
PM tasks
Mahadevan et
al. (2010)
Hybrid
GA/RSM as
statistical
analysis
Multi-unit
system (series
and parallel)
Optimal PM
interval based
on minimizing
total PM cost
Lin and Wang
(2012)
Fuzzy rules
Multi
-
unit
system
Proper PM
scheduling job
based on
minimizing total
PM cost
Khanlari et al.
(2008)
Markov Chain Single-unit
system
Optimal PM
interval
Tosun and
Kurüuzum
(2009)
Markov Chain
Single
-
unit
system
Optimal PM
interval
Panagiotiduo
and Tagaras
(2007)
ES and SA
algorithm
Multi-state
system
Improvement of
total production
and maintenance
costs
Fitouchi and
Nourelfath
(2014)
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Multi-objective
genetic
algorithm
(MOGA)
Multi-
component
system
Optimized
machine
availability at
minimum cost
Adhikary et al.
(2016)
Simulation SIMAN
simulation
Single-unit
system and
multi-
component
Assignment of
PM tasks
Knapp and
Mahajan (1998)
Matrix
formation
Grouping
- similarity
matrix
- ANOVA
analysis
Multi
-
unit
system (series)
Optimal PM
interval based
on group of
systems
Talukder and
Knapp (2002)
Multi-criteria AHP- multi-
criteria
evaluation
present in the
form of a matrix
Multi-unit
system
Assignment of
PM tasks based
on specific
components
Labib et al.
(1998)
5.2 Time-based
Time-based planning involves the measurement of the subject analysis in terms of the
allocation and information of a certain period or duration of an operation. The time basis is
crucial in planning, as it serves up an analysis which indicates the amount of time spent on
projects, usually in reference to periodic processes. In the PM planning literature, the analysis
encompasses a wide perspective, which requires a lot of information and data concerning
maintenance factors such as the time spent for repair and replacement, the time lost because
of failure, the time allocation for gathering spare parts, shift times and time for a system’s
operation. There are various methods conducted in regard of maintenance factors that would
affect the effectiveness of PM planning.
Methods grouped under the mathematical formulation category have received
tremendous attention from researchers into time-based PM planning. One of the methods
deployed is dynamic programming, which is known as an optimization method that
transforms complex problems into sequences of simpler problems. Dekker (1995) developed
a framework for an integration of optimization, which consisted of priority setting, planning
and combining PM tasks. In the priority setting, the order of PM tasks’ execution was
determined. The analysis involved a dynamic programming which depended on the rolling
horizon presented in the planning and combining of PM tasks. Multiple objective criteria and
the uncertainty of the PM interval led to optimal solutions. Hence, a properly scheduled job
for PM planning was the outcome of the analysis based on the optimal integration of the
combination of PM tasks and planning.
Wilderman et al. (1997) grouped PM actions using a dynamic program with a rolling-
horizon approach in order to prepare for a properly scheduled job, and thus they minimized
costs. The rolling-horizon approach for grouping PM actions was based on component usage.
The approach was founded upon long-term planning, that was to be updated easily by
incorporating short-term information that could change over time. However, due to the
complexity of practical situations, it was difficult to achieve real optimal solutions. Duffuaa
and Al-Sultan (1997) also used dynamic programming to formulate and obtain an
implementable solution with regards to PM actions and emergency maintenance. The authors
aimed to develop a schedule for job assignment by minimizing delays and maximizing the
utilization of resources such as manpower, spare parts and tools through the exploitation of
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integer and stochastic programming. The solution provided a decision-making tool for
assigning manpower, which was reserved for anticipated jobs based on the schedule
developed.
In addition, Zhao (2003) applied dynamic programming to determine the optimal PM
interval of a critical reliability level for a single-unit system with multiple components,
subject to degradation. The parameter adopted to undergo the analysis was the time interval
for the PM and its hazard rate. By using dynamic programming for the analysis, criteria such
as the hazard rate, reliability, availability and cost were compared with the operational time.
The outcome of the analysis could support the decision-making on PM actions based on the
value of the acceptable critical reliability level with an optimal time interval in the PM cycle.
Van et al. (2011) also presented a study of dynamic programming with the rolling horizon to
determine the effectiveness of grouping in PM planning for a multi-component system. The
authors took into account the PM durations and the scheduling of maintenance operations
occurrences. The authors further developed a methodology for dynamic programming with
positive economic dependence, which suggested that the grouping of PM actions was cheaper
rather than performing PM on components in isolation. Furthermore, the authors developed a
new algorithm by considering some opportunities with limited durations, which led to the
reaping of profits from PM actions in the grouping optimization procedure.
Other time-based planning mathematical formulation methods are linear, nonlinear and
mixed integer programming. These types of programming involve the computation of the
minimization or maximization of the value of an objective function such as time under a set
of constraints imposed by the nature of the problem being studied. Yao et al. (2004)
developed a MILP model for determining the planning through the assignment of PM tasks
according to a group of tools, by using a horizon planning approach in a single-unit system
with multi components. The authors also proposed a two-level hierarchical framework, which
consisted of PM planning at a higher level and PM scheduling at a lower level. The PM
planning captured both the stochastic failure process of the system and the demand pattern,
which were modelled for the long term. Meanwhile, PM scheduling was based on a short-
term model, which conformed with the PM planning model and delivered an optimal PM
schedule. The authors stated that the methodology was feasible and promising, which brought
benefits such as the increase of equipment’s availability and the ability to generate more
profit while eliminating human errors.
Su and Tsai (2010) presented an efficient method of flexible PM planning by
determining the period of PM and the sequencing of jobs simultaneously for a multi-unit
system in a parallel configuration, that minimized the time taken to complete all jobs, which
is known as the “makespan” of a schedule. By using MIP, the authors carried out studies on
two systems that were arranged in parallel with three different cases of unavailable periods (r,
which are r1 r2; r1 = r2 = r) and no waiting time was allowed for the two unavailable
periods. Due to the exponential time complexities, the computational results were quite
efficient for large problems. Thus, the decision on the PM planning could be made based on
the job scheduling and repair arrangements.
Moghaddam and Usher (2010) developed a non-linear MILP model on a single system
in order to determine the optimal PM interval as well as assigned PM actions. Cases that
considered PM actions were based on maintaining the system, replacing the system or doing
nothing. The method aimed to minimize the total cost subject to constraints on the system’s
reliability. The developed model could also be used to generate a new PM planning process
quickly, even after unexpected failures had occurred in the system; hence, PM scheduling
could be updated from time to time. Chen (2010) used ILP to deal with the sequencing and
setup time problems in a single-unit system’s state. The model was developed to minimize
the total setup time, subject to maintenance and due dates. However, the ILP model
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consumed more computation time and could only be used for small-sized problems when
compared to the heuristic approach.
Other methods such as the proportional hazard model (PHM) and Weibull distribution
have also received attention for determining an optimal PM interval in the analysis of time-
based PM planning. Kobbacy et al. (1997b) also used PHM for the state of a single system
with multiple components to find the optimal PM interval. During the planning, the authors
performed analysis by considering factors such as duration to perform PM and time to
experience failure when determining an expected cost within a time horizon. The result of the
optimal PM interval was presented by selecting a longer time (in days) with the lowest
availability of the system compared to other time in the cycle number. Caldeira and Guedes
(2007) furthermore, presented a Weibull hazard function to calculate the optimum frequency
to perform PM in a single-unit system with multiple components that arranged in a series
configuration. The calculation involves the interval time between PM actions for each
component, depending on factors such as failure rate, repair and replacement time for each
component in the system, thereby minimizing the PM cost. Thus, costs for breakdown were
minimized and the PM actions could be planned and assigned within the predetermined
interval.
Aside from mathematical formulation, AI has also been adopted for time-based PM
planning. Referring to the literature on PM planning, AI methods that have been applied are
fuzzy rules, GA, and Bayesian and heuristic Tabu searching. For example, Hennequin et al.
(2009) used fuzzy logic for a single repairable system that had undergone PM by accounting
for the impact of imperfections due to human factors. The authors used three membership
functions for comparisons such as technicians’ experiences, intervention times and the ages
of systems, which aimed to minimize the expected cost rate per unit of time and maximize
the availability of the system. The results of the cost and availability functions of optimal PM
were presented by comparing the optimal ages of PM actions through simulation methods to
build more accurate planning models for PM which considered human factors.
Tsai et al. (2001) incorporated a grouping method for mechanical components in a
single system with PM actions. The authors optimized the mechanical components based on
the failure rate and reliability to formulate an age-reduction model with the integration of
GA. In the complex numerical calculation concerning PM scheduling using GA, an optimal
combination of PM actions maximized the effects of systems in terms of their costs and lives,
thereby reducing the calculation time. Van et al. (2012) optimized both GA and MULTIFIT
algorithms based on the rolling horizon for grouping PM actions with the availability
constraint of having a limited number of maintenance personnel. The availability constraint
refers to the PM interval which indirectly affects a component’s useful life. Time-based
planning was conducted in a single-unit system with multiple components. Similarly to Van
et al. (2011), the authors applied a grouping of PM actions in four steps, namely individual
optimization, tentative planning, grouping optimization as well as updates and decisions.
However, their study differed in the sense that it used the MULTIFIT algorithm with the aid
of the GA to determine the minimum PM duration of each group with a minimum number of
maintenance personnel.
Lin and Huang (2010) used an AI model based on Bayesian methodology for a single-
unit system with multiple components that usually deteriorated with age. When making
decisions for PM planning, the authors determined an optimal non-periodic PM, which
minimized the total cost per unit of time. The model had to overcome difficulties in
performing analyses due to numerous uncertainties and the scarcity of data. Results of the
analysis were based on the impacts of the intensity parameters function and the effect of time
for PM actions that led to the attention to the critical factors for decision-making in PM
planning. Raza and Turki (2007) compared the effectiveness of two meta-heuristics such as
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Tabu searching and simulated annealing in relation to PM planning by focusing on
scheduling and job processing for a single-unit system’s state. Both algorithms were
performed in a numerical experimentation with large-scale problems, which provided a more
robust solution for minimizing total completion time. In the study, the characteristics of an
optimal schedule assisted in a more directed planning for PM considering the possibility of
system failure.
With their main aims being to improve system availability and system throughput
simultaneously, Wang and Liu (2015) investigated multi-objective parallel machine
scheduling problems with two kinds of resources; machines and moulds, and with flexible
preventive maintenance activities on resources. A multi-objective integrated optimization
method with version 2 of the non-dominated sorting genetic algorithm (NSGA-II) was
adopted, that integrated production scheduling and PM planning on machines and moulds
simultaneously. Results showed that that the integrated method outperformed periodic PM
planning in terms of multi-objective metrics.
Applications of simulations for time-based PM planning is the approach least covered
by the literature. However, that approach is very useful for obtaining significant result from
real-world systems’ operation and behaviour. The information obtained can be applied in PM
planning to improve system performance. Abogrean and Latif (2012a) presented a new
approach using Witness simulation for combating issues related to maintenance in a cement
industry. The issue they highlighted was the system breakdown due to deterioration caused
by age and usage that had led to the interruption of production. The authors developed a
simulation model and demonstrated it on a single-unit system by focusing on the spare parts
and maintenance personnel, with reference to the time-effective aspects of the situation.
Thus, better planning for PM could be implemented in the actual operation based on the
result of a simulation by improving the stock control system and ensuring efficient
communication and teamwork throughout the facility.
Also adopting simulation, Assid et al. (2015) proposed an effective joint production,
setup and maintenance control policy for inflexible and unreliable single machines that
produced two part types. The control policy was evaluated using a combined
continuous/discrete event simulation model; the hedging corridor policy (HCP) and the block
replacement policy (BRP). Aiming to maximize machine availability and to reduce the risk of
shortages, it integrated the concept of opportunistic maintenance by taking advantage of the
machine stoppages for setup operation to carry out preventive actions.
Overall, time-based planning has been used widely to evaluate the total anticipated time
for PM and its impacts in decision-making with regard to PM planning. It has been quickly
and easily applied from the planning perspective, but the value of time assigned for projects
can be varied and inconsistent. Thus it affects measurement and analysis, especially in the
real, practical world. The literature concerning time-based PM planning is summarized in
Table 6.
Table 6: Summary of time-based PM planning
Category
Method
System’s state
Outcome
References
Mathematical
formulation
Dynamic
program
(combined
activities)
Single
-
unit
system and
single
component
Proper PM
scheduling job
for grouping of
PM tasks and
PM planning
Dekker (1995)
Dynamic
programming
with rolling-
Single
-
unit
system and
single
Proper PM
scheduling job
for a group of
Wilderman et al.
(1997)
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horizon
grouping
maintenance
activities
component PM actions
Dynamic
programming
Multi-unit
system
Proper assigning
of manpower to
perform PM
Duffuaa and Al-
Sultan (1997)
Dynamic
programming
Single
-
unit
system
Optimal PM
interval
Zhao (2003)
Dynamic
grouping with
positive
economic
dependence
Single-unit
system and
multiple
components
Proper PM
scheduling job
based on
grouping of PM
actions
Van et al.
(2012)
MILP (group
tools)
Single-unit
system and
multiple
components
Proper PM
scheduling job
based on a
group of tools
Yao et al. (2004)
MILP Multi-unit
system (parallel)
Proper PM
scheduling job
and repairs
arrangement
Su and Tsai
(2010)
Non
-
linear
MILP-cost
Single
-
unit
system
Optimal
PM
interval and
assignment of
PM tasks
Moghaddam
and Usher
(2010)
ILP
Single
-
unit
system
Optimal PM
interval based
on minimized
total setup time
Chen (2010)
Proportional
hazard method
(PHM)
Single
-
unit
system
Optimal PM
interval
Kobbacy et al.
(1997b)
Weibull
distribution
Single-unit
system and
multiple
components
Assignment of
PM tasks
Caldeira and
Guedes (2007)
Genetic
algorithm
Multi
-
component
Optimal PM
interval
Mabrouk et al.
(2016)
Artificial
intelligence (AI)
Fuzzy logic
Single
-
unit
system
Proper
scheduling job
based on human
factors
Hennequin et al.
(2009)
GA Single-unit
system and
multiple
components
Optimal PM
interval based
on grouping of
components
Tsai et al.
(2001)
MULTIFIT
algorithm with
GA (grouping)
Single-unit
system and
multiple
Optimal PM
interval based
on grouping PM
Van et al.
(2012)
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components actions
Bayesian Single-unit
system and
multiple
components
Assignment of
PM tasks
Lin and Huang
(2010)
Tabu
search/simulated
annealing
Single-unit
system
Proper PM
scheduling job
based on
minimization of
total completion
time
Raza and Turki
(2007)
NGSA
-
II
Multi
-
unit
(parallel)
Optimal
production
scheduling and
PM
Wang
and
Liu
(2015)
Simulation
Witness
simulation
Single
-
unit
system
Assignment of
stock control
system prior to
performing PM
Abogrean and
Latif (2012)
HCP & BRP Single-unit
system
Reduction of
risk of shortage
Assid et al.
(2015)
5.3 Failure-based
Another basic analysis for deriving the best PM planning, failure-based planning,
involves analysis that takes into account information about system or component
deterioration. Literature on failure-based PM planning has been reviewed and involved a
detailed definition of failures that occur with systems or components before further analysis
is carried out. There are several failure-based planning methods that would affect PM’s
overall effectiveness. In failure-based planning, in order to anticipate the characteristics of a
failure mechanism, critical analyses involving the objectives of analysis and evaluations of
failure issues are carried out and extensively quantified. This should ensure that PM is
conducted properly, thus reducing failure. Methods of critical analysis commonly used are
tree diagrams, failure mode and effect analysis (FMEA) and failure mode effect and critical
analysis (FMECA).
Ab-Samat et al. (2012) carried out a case study on planning for PM with the aid of tree
diagrams. The authors outlined the problems faced by the company in their case study which
included the insufficiency in numbers of maintenance staff available to perform PM and the
systems breakdowns that led to inefficient PM planning. Based on the data of prior failures
and system breakdowns, a root cause analysis of the issues involved in the ineffective PM
was presented as an affinity diagram. Then, a PM planning model was developed and the
analysis was presented in the form of a tree diagram that enabled possible solutions to be
generated. The proposed solution that was validated at the company studied provided for the
maintenance staff to perform PM by focusing on the critical systems instead of the non-
critical systems which improved their PM processes.
In another study, Ahmad et al. (2011) conducted a case study on a single-unit system
with a single component in a processing industry. The authors developed a PM model that
consisted of three general steps which were problem identification, evaluation of the current
system condition and maintenance decision. FMECA was used in the model developed to
identify possible external factors that contributed to the component failure. Once the external
factors and failure modes were identified, the critical component was evaluated using a PHM
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before determining the PM interval in the current system state by means of an age
replacement model. Thus, the replacement of components based on the PM interval for the
current system state implied a reasonable decision to improve system reliability.
Cicek et al. (2010) also proposed a failure analysis methodology for PM planning,
where the evaluation was conducted using a reliability model, which permitted the use of
flexible intervals between maintenance interventions. The authors focused on the failures and
accidents to achieve the best possible safety levels at the lowest possible cost. A case study
was conducted on the state of a single unit with multiple components in a fuel oil system in
the marine engine industry. Evaluation of the failures using FMEA analysis was structured
based on feedback, brainstorming and expert judgment resulting in PM planning that
increased the reliability of the system. Therefore, PM tasks can be assigned properly based on
the priority of critical components.
Khan and Haddara (2003) presented a new, critical-based maintenance methodology for
decision-making in PM planning by integrating the issue of reliability with safety as well as
with environmental issues. The critical-based method involved risk and failure analyses that
were intended to inform decisions about the required level of system design features, and to
evaluate the system’s safety and risk. The authors validated their developed methodology in a
study of multi-unit system states for heating, ventilation and air-conditioning (HVAC) units.
The methodology comprised of three main modules; estimation, evaluation and planning. In
the estimation module, the consequences as well as the probability of failure were identified.
The evaluation module consisted of aversion and acceptance analyses which reduced
incidences of failure amongst components prior to designing the PM planning. This was done
to reduce the level of risk resulting from system failure in the final module of PM planning.
Based on the results obtained from the analyses, the PM interval time for the system was
determined.
Almomani et al. (2012) presented a matrix formation known as SCM to group similar
maintenance requirements together by focusing on the system failure types involved before
the formation of virtual cells. The authors studied multi-unit systems in the mining industry.
The major focus of the study was to group procedures by using SLINK; an example of SCM,
to determine which systems could be paired by reference to their failure types. During the
grouping process, duplicated systems and systems with variations of failure types were
considered as seed systems in order to construct a matrix table of initial system failures. The
results were presented in the form of a dendrogram that represented the similarity between
systems before they were virtually grouped. The author stated that the grouped systems in the
virtual cells indicated that creating a standard process plan with optimized inventory spare
parts was an economical tool to use, especially during the phases of PM execution.
Al-Mishari and Sulaiman (2008) designed an optimal PM schedule based on failures
that corresponded to the faster degradation of a system. The study was conducted on a single-
unit system with multiple components. The authors focused on the impact of the failed
accumulator on the degradation rate of a mechanical seal. In order to form an equation model
using a load-sharing method, two parameters were taken into account to facilitate the
analysis, namely the failure and repair rates. The parameters were modelled using the
equation for systems affected by two components and they were presented in matrix form.
With the aid of regression analysis, an optimal plan for PM on the components was presented
by simulating different frequencies for each component in terms of the PM schedules.
Overall, according to the review of failure-based planning, prior to any further
evaluations of failure, thorough assessments and analyses are crucial for defining the
criticality of failure. This is because failure evaluation may have an impact on decision-
making that pertains to PM planning. However, it is difficult to evaluate failure as the subject
matter of any analysis due to the difficulty of gathering information on the time lost and the
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costs paid due to failures. Having said that, even though determining the root causes of
failure mechanisms is time-consuming, it would affect further analysis if a failure’s cause
was not properly determined, especially in real-world scenarios. The literature concerning
failure-based PM planning is summarized in Table 7.
Table 7: Summary of failure-based in PM planning
Category
Method
System’s state
Outcome
References
Critical analysis
Tree diagram
analysis
Multi
-
unit
system
Assignment of
PM tasks based
on the criticality
of systems
Ab
-
Samat et al.
(2012)
FMECA, PHM
and age
replacement
model
Single
-
unit
system and
single
component
Optimal PM
interval based
on critical
components
Ahmad et al.
(2011)
FMEA
Single
-
unit
system and
multiple
components
Assignment of
PM tasks based
on the priority
of critical
systems
Cicek et al.
(2010)
Critical
-
based
analysis
- probabilistic
failure
- consequence
Multi
-
unit
system
Optimal PM
interval
Khan and
Haddara (2003)
Matrix
formation
Similarity
coefficient
matrix
Multi
-
unit
system
Assignment of
PM tasks in
groups of
systems
Almonani et al.
(2012)
Load
-
sharing
and regression
analysis
Single
-
unit
system and
multiple
components
Proper PM
scheduling job
based on failure
components
Al
-
Mishari and
Sulaiman (2008)
6. Literature assertions
PM is one of the maintenance actions that is normally applied in industry to ensure that
systems get to perform their intended functions for a long time. Effective PM has proven to
have a positive impact on organizations’ operations and profits. According to the survey
above, each of the literatures concerning PM planning was studied by discussing the elements
underpinning the PM planning concept, namely the objectives, the states of systems and the
methods applied. Gaps in the available literature and the justifications for these have been
stated in the literature findings. They can be discussed further from three perspectives: the
trends in the PM planning used, analyses of PM planning elements; and remarks for the
direction of future research. Each of these perspectives will be briefly discussed in the
subsections that follow.
6.1 Trend of PM planning
PM planning has been reviewed in terms of cost-based, time-based and failure-based
planning addressing the objectives of PM, the states of systems and the methods applied.
Reviews of each of the planning categories has been depicted in the forms of trend charts to
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present the planning-based outcomes by year. The trends have been charted based on the
research published, which are summarized for each review. Thus, two elements have been
selected to represent PM planning, namely the planning-based terms and the methods applied.
The planning-based trends in PM planning have been charted based on the objectives of PM
planning by year, whereas the trends for methods in PM planning have been charted out
based on the classification of methods by year. Hence, the trends in PM planning and in the
methods applied are depicted by year as shown in Figures 1 and 2 below respectively.
Referring to Figure 1, the planning-based literature was reviewed from 1995 to early
2016. From the observation of all planning-based studies, most researchers tended to
optimize PM planning by calculating costs and time, theoretically to achieve their planning
objectives. Even though the maintenance factors of cost and time can ease the calculation of
the PM planning’s objectives and direct investigators towards them, both cost and time are
required to create reliable data and information before further measurement and analysis can
be conducted. By contrast, failure-based planning received less attention and, thus, was
scarcely found in the literature. From the review of failure-based planning, most of the
literature denotes failure as an integer number which represents the availability of the failure
of the system or component. Some researchers have interpreted failure in terms of the cost
and time required to determine the complex calculation. However, without a proper
definition, the failure that occurs may leave an impact on the data processing and analysis,
both theoretically and practically. This is because the information about failure obtained in
the form of data acquisition from the performance of the system is an aid for planning future
actions and making decisions in relation to PM and taking improvement actions (Duarte et
al., 2013).
The planning-based trends in PM planning reflect the changes in research concerning
PM planning over time. The charts can be used to show the achievement of objectives in the
research about PM planning and the planning-based studies that have received major
attention from PM planning researchers. Similar trend can also be depicted in terms of the
PM methods applied.
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Figure 1: Trends in planning-based PM from 1997 to 2016
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1997
1998
2002
2003
2005
2006
2007
2008
2009
2010
2012
2014
2016
2003
2008
2010
2011
2012
2014
1995
1997
2001
2003
2004
2007
2009
2010
2012
2015
Cost-based Failure-based Time-based
Frequency of outcome
Planning-based Preventive Maintenance
PM interval PM schedule job PM tasks
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Figure 2: Trends in methods applied in PM planning from 1997 to 2016
Figure 2 shows the frequency with which different methods have been used in PM
planning according to the extant literature for the years from 1995 to early 2016. It can be
seen that AI and mathematical formulation have received a tremendous amount of attention
from researchers setting out to achieve PM planning objectives. Compared to other methods,
AI and mathematical formulation require complex calculations in providing great results
theoretically speaking, but causing difficulties in terms of real industrial practice. For
example, implementing AI requires the use of experts and software for analysis. This can lead
to expensive and unreliable systems being implemented due to the fact that relevant parties
have to invest in software and training to utilize it properly.
Meanwhile, mathematical formulation is dependent on data, and data problems in the
real world will affect the real operational status of a process. For example, unrecorded data
might be a major issue when locating documentation, or the required data might be too
specific or too uncertain. Furthermore, numerical examples from mathematical formulations
are created for application in the development of PM planning approaches. However, the
values used in the approach adopted will affect real-world conditions qualitatively, so their
practicability and the reliability of their final outcomes may be viewed with some suspicion.
According to Wang and Hwang (2004), some practical problems regarding maintenance
policies and planning cannot be fitted to mathematical models due to the simplified
assumption made during mathematical modelling development. Hence, this would lead to
unrealistic and inapplicable results, which might affect actual process operations. These are
the reason why both AI and mathematical formulation get minimal interest and take-up in
most industries.
Similarly, simulation is also less preferred by industries as it requires the acquisition of
related simulation software that requires special training to run it. Even though simulation is
very useful for describing maintenance issues, it still cannot replicate reality fully. This is
because the accuracy of data would be a major issue among practitioners as it has to reflect
0
1
2
3
4
5
6
7
8
1995 1997 1998 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2014 2015 2016
Frequency of methods used
Year
Artificial Intelligence (AI) Critical analysis Mathematical formulation
Matrix formation Multi criteria Simulation
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real-world problems. Hence, this will affect decision-making for future recommended
actions. Besides that, multi-criteria categorization is very complex because it involves
making quantitative comparisons across multiple alternatives (Norgard, 2003). Multiple
alternatives are usually evaluated based on their strengths and weaknesses by considering all
conflicting criteria and decision-makers’ judgments. However, analysis for this method
requires higher levels of effort because the information concerning each potential solution is
essential for accurate comparisons of information regarding each potential solution. Thus,
additional time and resources are required, as high-priority needs, for performing this
method.
Critical analysis entails both qualitative and quantitative methods because it involves
detailed analysis and is focused on the causes of failure (Al-Najjar and Alsyouf, 2003). The
characteristics of failure qualitatively describe the type, causes and consequences of failures.
Then, the failure is quantified by ranking or assessing its criticality level based on human
judgment. This can be performed by referring to knowledgeable and experienced personnel
without any need to purchase software or initiate training. Moreover, the critical analysis
method is preferable for assessing real-world practices because the accuracy and reliability of
data obtained are not major problems. Practitioners can derive data from their actual
operations. Besides that, matrix formation is a simple method that can be easily interpreted. It
involves an uncomplicated and understandable mathematical formulation based on variables
and functions. The matrix formation is also a concise and useful way to represent the
relationships between variables and functions in various forms, such as L-shaped, similarity
and incidence matrices. This method also requires specific information and careful
verification because it may result ultimately in erroneous applications as the outcome. Hence,
the application of the matrix formation in industrial practices can be said to be relevant and
convenient as it does not require practitioners to understand complex mathematical
formulations.
Trends in PM planning have shown a growth in the methods that had been used over
time. The chart above has been used to show the transformation in the deployment of the
various methods used in PM planning research and those methods that have received major
attention from PM planning scholars. Hence, it has been important to show the development
trends of suitable and practical methods for achieving the objectives of PM planning.
Considering the literature’s findings is the analysis of PM planning will be discussed in the
following sections.
6.2 Analysis of PM planning
Another perspective from which to view PM planning concerns the analyses conducted
in the previous literature. The analyses are described to highlight three essential points taken
from the PM planning literature which are the planning-based methods, the grouping
approach and the practicability of the planning-based techniques. Despite there being issues
with the practicability of the aforementioned PM planning methods , Dekker (1996)
highlighted the gap between theory and practice in relation to developed planning
approaches. Thus, the limitations and the gaps between theory and practice for each aspect of
PM planning will be explained briefly.
Planning-based
According to the discussion in a previous section about trends in PM planning, failure-
based planning had received less attention than cost-based or time–based studies. This was
due to the limitations on interpreting failure data and information as the basis for analysis
(Vaughan, 2005). However, failures and system breakdowns that occur in actual operations
are the main maintenance issues that receive tremendous attention, both theoretically and
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practically speaking. This is because a system is recognisably an essential element of
production floor processes whose failure may affect an organization’s management and its
operations (Wang and Hwang, 2004). Hence, both the cost-based and time-based approaches
are limited by leading to there being a lack of the theoretical knowledge that ought to be
uncovered in order to tackle the real-world problems being confronted by businesses. Thus,
failure-based research can play a useful role by revealing reliable data from actual failures
when they occur and by directing the analysis towards optimal PM planning for real-world
applications.
Grouping approach
In the literature, PM is comprised of a multitude of different aspects too broad to
facilitate the introduction of general approaches that might cover all possible cases.
Researchers have only focused on PM operations without analyzing the implementation of
planning, to see if it is suitable for the systems studied. Therefore, the related planning for
PM should be developed and examined in order to maximize the effectiveness of PM
operations. From the previous discussion regarding trends in PM planning methods applied,
the grouping approach was suggested because it is able to direct knowledge practically
towards the creation of optimal PM plans. In general, as a group technology (GT) concept,
grouping has been of growing interest in related maintenance environments. It may facilitate
decision-making and influence planning by generating more accurate results (Dekker et al.,
1997; Kellerer et al., 2013). Grouping is also an easy and simple approach for conducting
planning-for-PM actions since the number of groups indicates the number of maintenance
actions involved (Kuo and Yang, 2008; Rustogi and Strusevich, 2012). Previous literature has
recorded that the scope of grouping covers the process of identifying similarities or
recognizing identical characteristics amongst maintenance actions, systems or components
and spare parts or tools, which should be evaluated and analyzed during planning-based
subject analysis (Talukder and Knapp, 2002). Furthermore, the grouping approach in the PM
planning has garnered attention from researchers as it provides various benefits such as
simplifying maintenance actions, aiding mathematical analysis and creating a standard
process plan which can lead to time and cost savings.
Practicability of the planning-based approaches
Throughout the review undertaken, it was noted that the developing PM planning
usually focused on determining how much PM should be carried out on related systems or
components, and how frequently PM should be performed on related systems or components.
The process of planning for PM is complex due to the fact that many parameters and
maintenance factors need to be considered. In order to develop a practical PM plan, its
feasibility and practicability in an industry needs to be considered, especially when dealing
with failures and system breakdowns in actual operations. Therefore, determining the
practicability of planning-based PM is an outcome which reflects the fundamental
requirement for planning to support the robustness of the analysis that may lead to the
development of optimal PM planning. In the literature, the methods applied to solve
maintenance issues often provide results, which achieve the objectives of PM planning
without proving the practicability and reliability of the results. As an example, from the
operational perspective, the issue of system breakdowns is normally associated with failure,
the understanding of which is fundamental for planning future actions in actual operations.
The grouping approach has been adopted to simplify analysis, since the systems are
grouped together due to their similar failure characteristics. Hence, the grouping analysis
conducted requires supportive evidence derived from the performance of a cost-benefit
analysis for the group of systems or components studied prior to the allocation of a time
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schedule for PM. Analyses related to cost and time are inevitable parts of various types of
research as the results provide some supportive evidence about the work’s economic
advantages prior to decision-making (Löfsten, 1999). Cost-benefit analyses are simple and
convenient for practitioners to undertake when they involve the computation of the actual
costs of maintenance in conjunction with the maintenance duration. Hence, the results of
cost-benefit analyses will assist decision-making processes in the sense that they can provide
outcome which reflect the practicability of suggested solutions in the real world.
7. Conclusion and remarks for future research direction
Based on the discussions derived from the literature’s assertions, it has been identified
that most of the applied PM methods have tended towards the development of complex
mathematical equations in order to solve maintenance issues. However, according to
Almomani et al. (2012), relatively few studies have focused on the determination of optimal
PM planning. The authors have advanced a method for simplifying maintenance operations
by grouping systems, which ca enhance PM planning. Yet, the condition of systems should
be taken into consideration when developing an optimal plan prior to the ratification of a
grouping process. Therefore, it is preferable to have a model or framework to act as guidance
in the proper procedures necessary for optimal PM planning.
The direction of future research should focus mainly on the development of frameworks for
two major aspects of PM: the approach to implementing it and the creation of environments
that enable planners to cope with industrial practices. Grouping seems to be a practical
suggestion that can be implemented. A grouping of related systems and failures in a
systematic and comprehensive way could lead to the solving of maintenance issues.
Environmentally speaking, the practicability of optimal PM planning should be tested and
validated during actual operations, particularly by the establishment of case studies in real
industrial settings.
Moreover, the states of systems should be considered in their environmental settings
as that would also influence the analysis of any proposed PM framework. In the literature, the
state of a system is an indicator of the condition of a system because it influences the analysis
procedure for solving maintenance issues during PM planning. As discussed in Section 2.4.2,
the state of systems is divided into two categories, i.e. single-unit and multi-unit systems.
Single-unit systems have received more attention from past researchers compared with multi-
unit systems. This is due to the complexity of developing maintenance models through
mathematical formulation which is the basis for developing a maintenance model for multi-
unit systems (Ab-Samat and Kamaruddin, 2014). However, the concentration on single-unit
systems may not serve to show the significance of PM planning for tackling real-world
maintenance problems (Lin and Wang, 2012), since multi-unit systems are interdependent
structures and problems occurring on one system may affect its components and those of
other systems as well (Khanlari et al., 2008). The complexity and flexibility of multi-unit
systems which are more suitable and applicable for study as real-world environments involve
multiple systems running production processes alongside each other.
Therefore, a detailed framework which incorporates reliable data and a grouping
procedure at the system level needs to be proposed. In fact, many relevant decisions related to
PM actions on systems or components can be made, where reliable data and grouping can
serve as PM planning analysis procedures. The result will be more practical PM planning
which provides more accurate and better updated inputs. With the aid of such a framework,
maintenance management could apply it as a standard procedure plan for effective PM
operations. Recommendations for future work are as follows:
The calculation of cost-benefit analyses should be rendered much simpler in order to
reduce the limitations on setting up the mathematical assumptions on which they are
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based. This is because the more assumptions yield more computability due to
overlook at details to make necessary computation. These assumptions are set up to
reduce the fidelity of mathematical models to real-life contexts. Cost-benefit analyses
should also consider the costs of manpower and spare parts which will affect the
whole total maintenance cost due to different systems consisting of different
components and spare parts.
Developing computer-based integrated PM planning and scheduling based on
simulation and GT would be helpful and assist more practically with making
decisions about PM planning on systems in clusters. Thus, incorporating PM planning
into production scheduling should be considered in order to achieve better planning
results in terms of systems performance and productivity.
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Biographical Details
Ernnie Illyani Basri received her B.Eng. (Hons.) degree in manufacturing engineering with
management from Universiti Sains Malaysia in 2012 and has completed her M.Sc. in the
School of Mechanical Engineering at the University Science Malaysia (USM), in 2014.
Currently pursuing a Ph.D. at Universiti Putra Malaysia, her research interests are
manufacturing systems and maintenance management.
Izatul Hamimi Abdul Razak graduated with an M.Sc. in 2010 from the School of
Mechanical Engineering at University Science Malaysia (USM) and currently is a lecturer in
the Mechanical and Manufacturing Section at Universiti Kuala Lumpur Malaysia France
Institute. Her research interests include manufacturing systems, maintenance management
and product design.
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Hasnida Ab-Samat received a B.Eng. (Hons.) degree in manufacturing engineering with
management from Universiti Sains Malaysia in 2007 and completed her M.Sc. in 2010 and
then graduated with a Ph.D. in 2015 from the School of Mechanical Engineering at the
University Science Malaysia (USM). Her research interests include industrial engineering,
manufacturing systems and maintenance management.
Shahrul Kamaruddin received a B.Eng. (Hons.) degree from the University of Strathclyde,
Glasgow, Scotland in 1996, an M.Sc. degree from the University of Birmingham, U.K., in
1998, and a Ph.D. from the University of Birmingham in 2003. He is an Associate Professor
at the Mechanical Engineering Department, Universiti Teknologi PETRONAS (UTP),
Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia. He also has various past experiences
of manufacturing, from heavy to electronics industries, especially in the field of industrial
engineering, manufacturing processes and product design. He has written more than 160
publications for reputed national and international journals and conferences.
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... To counter this, preventive maintenance (PM) could be employed, which follows a periodically scheduled maintenance plan to reduce unplanned maintenance. However, this leads to unnecessary downtime, as often times the maintenance is not required [6][7][8]. This could be resolved if predictive maintenance (PdM) could be achieved. ...
... The dimensions of the SW, specifically the width (SW Width ) and length (SW Length ), are contingent upon the number of rows (the number of parameters taken, i.e., M = 82) and the length of each individual row (the number of time logs taken, which is to be l = 24), respectively. The size of the SW (SW Size ) can be mathematically represented as equation (8). The stride is configured to be one unit, which implies the SW will move one log step at a time, creating a 2D input structure. ...
... This includes corrective maintenance, responding to unexpected failures through planned or unplanned interventions, with planned corrective maintenance offering strategic efficiency and safety advantages. Preventive maintenance focuses on scheduled tasks to prevent equipment failures and degradation, optimizing reliability, lifespan extension, and safety Basri et al. (2017). ...
... The findings align with previous research emphasizing the importance of sensor selection and predictive modelling in enhancing maintenance strategies. For instance, studies have highlighted the role of sensor compatibility and data transmission capabilities in effective predictive maintenance Zonta et al. (2020); Basri et al. (2017). Our study reinforces these insights by demonstrating the practical application of these principles in a real-world industrial setting. ...
Article
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Introduction This study explores the shift toward predictive maintenance through real-time data analytics to minimize machine downtime and improve machinery insights in industrial environments. Predictive maintenance aims to enable proactive interventions by predicting failures, enhancing operational efficiency. Methods The research was conducted in three stages. First, BA Glass equipment was sensorized using OPC Router and PowerStudio SCADA to facilitate real-time data extraction. A predictive maintenance algorithm was then developed in Python to analyze sensor data, predict failures, and trigger alarms. Finally, various forecasting models, including Linear and Polynomial Regression, Simple and Double Exponential Smoothing, ARIMA, and Prophet, were evaluated using a combination of blocked cross-validation and rolling window methodologies. The algorithm calculated performance metrics such as MSE, RMSE, and MAE for different parameter configurations and training sizes. Results A comparative analysis between wired and wireless sensors concluded that wireless sensors, although more expensive, were more practical and interchangeable in the factory setting. The results from the evaluation of prediction models showed that the Double Exponential Smoothing (DES) model with an additive damped trend and linear models performed best for datasets with daily seasonality and gradual oscillations. For datasets with stable trends and higher frequency oscillations, ARIMA and Prophet models proved to be more accurate. Discussion These findings suggest that the choice of sensors and prediction models can significantly impact the effectiveness of predictive maintenance systems. Wireless sensors offer long-term benefits in terms of flexibility and practicality, while the DES model and ARIMA/Prophet models are optimal depending on the dataset characteristics. This research highlights the value of real-time data analytics and predictive models in industrial environments for reducing downtime and improving decision-making.
... This type of maintenance requires set of essential tasks consisting of scheduled inspections that are regularly conducted to identify signs of wear and tear, corrosion, or other issues that may lead to equipment failure. These inspections allow maintenance personnel to detect problems early and address them before they escalate [53], along with routine servicing activities such as lubrication, cleaning, and adjustments to ensure that equipment operates smoothly and efficiently.It helps preventing friction, overheating, and other mechanical issues that can compromise performance. Also, replacement of wear parts, components that are subject to wear and tear, such as filters, belts, bearings, and seals, is done at regular intervals as part of preventive maintenance [54]. ...
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Full-text available
The studied process concerns a wet grinding and cycloning unit, set up to ensure that the particle size it processes complies with the requirements in terms of pipe transport mode. Frequent shutdowns, low reliability, the risk of delivering a product with inappropriate particle size, and the lack of operational parameters’ control are all obstacles for the unit, having enormous repercussions on its relevance and performance. To improve this situation, the implementation of an innovative 5-month quality and process control study has proven crucial to establish a culture of quality among operators, aiming to achieve the targeted grain size. However, the innovative aspects of our approach lie in its thoroughness and inclusiveness since it addresses the process both qualitatively and quantitatively, focusing not only on theoretically modeling and simulating the grinding circuit but also providing an integrated method to address every component of the unit. By going through the seven stages of this study, we were able to enhance the overall performance of the unit controlling the particle size at the output. After establishing a set of quality control tools, we observed a remarkable development in mastering various parameters influencing the targeted particle size. The benefits generated by the study are extremely satisfying for the unit at different levels, particularly in terms of quality, process control, and resource optimization. Furthermore, the integration of maintenance management into the process has proven essential to ensure the reliability and sustainability of the installation, thereby contributing significantly to the overall efficiency of the unit. Graphical Abstract
... Preventive maintenance (PM) [3] is a series of maintenance actions carried out without equipment malfunction or damage. PM prevents functional failures by conducting systematic inspections, tests, and replacement to maintain the equipment in the specified condition. ...
... In contrast, preventive maintenance deals with scheduled interventions, based on timeframe projections of the components, with the goal to minimize unplanned downtimes. However, this strategy can also lead to significant expenses due to unnecessary repairs and planned downtime events [4]. ...
Preprint
Full-text available
The maritime industry heavily relies on vessel maintenance to ensure the operational integrity and safety, as it is responsible for transporting more than 80% of global trade. Despite the industry's strong need for efficient maintenance techniques, there has been a noticeable gap in research regarding the use of data-driven methods to enhance vessel reliability. This study seeks to fill this gap, by examining the feasibility of deploying deep learning models to predict the Remaining Useful Life (RUL) of Heavy Fuel Oil (HFO) purification systems, taking into account also the challenges of the limited computational resources available on maritime vessels, as well as the substantial costs associated with implementing such models. Towards this direction, the impact of various optimization techniques (early stopping and pruning) on three state-of-the-art models (Long Short-Term Memory Network, Convolutional Neural Network, Autoencoders) was evaluated using operational vessel data provided by Laskaridis Shipping Co. Ltd., demonstrating the feasibility of deploying predictive maintenance (PdM) systems in real-world edge-constrained marine settings, potentially transforming maintenance practices and reducing operational costs.
... Berdasarkan penelitian Liu & Wu [9] bahwa dengan preventive maintenance dapat menurunkan downtime mesin. Penelitian Basri et al [10] meningkatkan performa mesin dengan preventive maintenance. Penelitian ini bertujuan untuk mengetahui penyebab terjadinya downtime akibat kerusakan komponen dan menurunkan downtime akibat kerusakan komponen pada mesin auto front wheel. ...