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Preventive Maintenance Checklist towards Effective Maintenance System: A Case Study in Semiconductor Industry

August 2013
Advanced Materials Research 748:1208-1211

DOI:10.4028/www.scientific.net/AMR.748.1208

Authors:

Hasnida Abdul Samat

Universiti Sains Malaysia

Ernnie Illyani Basri

Universiti Putra Malaysia

Show all 5 authorsHide

Preventive maintenance (PM) is the basic policy in maintenance system and widely used in companies all over the world. However, its advantages are not always fully received by the company because it is not carried out according to prescribed planning and schedule. This is because of improper implementation of the PM that leads to the major problem of increasing number of machine downtime. One of the reasons for the problem is ineffective PM checklist used during the implementation. Therefore, the aim of this paper is to reduce the planned downtime for PM by analyzing and enhancing the PM checklist. This is to show the importance of having good PM checklist as it simplified technicians workload and can reduce machine downtime if conducted properly. Consequently, good PM checklist will show the performances of the technicians doing their tasks as well as the performances of the machines to be operated in good operational condition.

Current PM Checklist for critical machine

…

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Preventive Maintenance Checklist towards Effective Maintenance

System: A Case Study in Semiconductor Industry

AB-SAMAT Hasnida

, BASRI Ernnie Illyani

, HARUN Nurul Aida

WEE Siew Ching

and KAMARUDDIN Shahrul

School of Mechanical Engineering, Universiti Sains Malaysia (Engineering Campus),

Seri Ampangan, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia.

hasnida_absamat@yahoo.com,

ernebasri@yahoo.com,

aidakimono2432@gmail.com,

qing.03@hotmail.com,

meshah@eng.usm.my

Keywords: Preventive Maintenance, PM checklist, Case Study, Semiconductor Industry

Abstract. Preventive maintenance (PM) is the basic policy in maintenance system and widely used in

companies all over the world. However, its advantages are not always fully received by the company

because it is not carried out according to prescribed planning and schedule. This is because of

improper implementation of the PM that leads to the major problem of increasing number of machine

downtime. One of the reasons for the problem is ineffective PM checklist used during the

implementation. Therefore, the aim of this paper is to reduce the planned downtime for PM by

analyzing and enhancing the PM checklist. This is to show the importance of having good PM

checklist as it simplified technicians’ workload and can reduce machine downtime if conducted

properly. Consequently, good PM checklist will show the performances of the technicians doing their

tasks as well as the performances of the machines to be operated in good operational condition.

Introduction

To cope with intense competition in today’s manufacturing industries, companies fight to be in the

front line by investing in highly automated production system with excellent and high-technology

equipments. Therefore, it is essential for maintenance system as a major role to ensure that the whole

system running smoothly and effectively. Maintenance is a part of manufacturing function that

contributes to sustain the performance of equipment as well as improve the operation efficiency in

manufacturing plant [1]. Maintenance is considered a non-value added process in manufacturing

industry. Nevertheless, it is undeniable that maintenance is one of the asset management processes as

it gives benefits in term of profit to the company as well as exceeds customer satisfaction [2]. The

scope of maintenance management cover every stage in the life cycle of technical system including

plant, machinery, equipment and facilities such as specification, acquisition, planning, operation,

performance evaluation, replacement as well as disposal [3].

Preventive Maintenance (PM) and Checklist

Preventive Maintenance (PM) is the most basic maintenance system that all companies should

apply.PM is encompasses by all the activities that necessary to keep facilities in good operational

condition. It is an action involving inspection, servicing, repairing or replacing physical components

of machineries, plant and equipment by following the prescribed schedule. PM on machinery and

equipment is usually carried out in manufacturing system as its role to prevent or slow down its

deterioration [4]. Instead of applying the basic corrective actions when failure happened, detailed PM

activities will allow for repair and replace of equipment before breakdown occurs. Basically, PM

activities are scheduled based on the fixed predetermined interval such as weekly, monthly or even

yearly. In order to develop a comprehensive PM program, it is crucial to have procedures of PM in

form of checklist [5]. PM checklist should contain precise tasks and schedule according to

equipment’s requirement and history data of failures. Basic elements in planned and scheduled

Advanced Materials Research Vol. 748 (2013) pp 1208-1211

doi:10.4028/www.scientific.net/AMR.748.1208

www.ttp.net. (ID: 202.170.51.226, Engineering Campus Universiti Sains Malaysia, Nibong Tebal, Malaysia-04/06/13,05:22:55)

activities involve task action steps, labour resource requirements, designation of time period to

perform the tasks, work’s priority and issuance of work order [6]. This paper will discussed the

development of PM checklist in achieving effective maintenance system in a case study company.

Case Study

The analysis is conducted in a semiconductor company located in Penang, Malaysia. The production

floor of this semiconductor company consists of 109 machines with only 8 maintenance personnel for

maintenance work. The maintenance personnel who are responsible according to four clusters are also

responsible for the facility department. Since the workloads is exceeded the man power availability;

therefore, a proper and effective PM checklist indeed can help the maintenance staffs to take care of

all the machines in order to reduce the machine downtime due to ineffective of PM checklist. A series

of phases for developing an effective preventive maintenance checklist are proposed in order to

achieve the main objective in this work which is reducing the machine downtime in company plant

due to ineffective of previous preventive maintenance checklist.

Phase I: Identify the critical machine. Data of machine breakdown time or machine breakdown

frequency are used to identify the critical machines. Machine which has got the highest machine

downtime and highest machine breakdown frequency are identified as the critical machine. For this

case study, Etching and Stripping machine is selected for further analysis.

Phase II: Study and identify the problem of current Preventive Maintenance (PM) checklist for

the critical machine. The current PM checklist for the critical machine is collected from the

maintenance department, and the checklist is analyzed thoroughly in order to figure out the

incompleteness of the current PM checklist. The problems of conducting PM checklist are listed

down in order to make an improvement or correction for it. Figure 1 shows the current PM checklist in

the semiconductor company.

PREVENTIVE MAINTENANCE CHECKLIST

Equipment: Etching & Stripping Machine Month: ________

Equipment no: 3020

Location: Wet Process

PM Duration: Weekly & Monthly

No Action Duration WW Remarks

1 2 3 4 5

1 Check all piping for any leakage Weekly

2 Check conveyor gear Weekly

3 Check and clean filters or strainers Weekly

4 Check all motor Weekly

5 Check all pressure gauge Weekly

6 Check and services all pumps Weekly

7 Clean and check main panel box Monthly

Done by: ...........................(Maintenance Personnel)

Date: ...........................

Verified by: ...........................(Engineer/ Supervisor)

Date: ...........................

Figure 1: Current PM Checklist for critical machine

From the current PM checklist showed in Fig. 1, some problems in the checklist have been

discovered.

1. Firstly, only few of the parts that needs to be maintained for critical machine is listed in the

checklist. Therefore, those parts that missed from checklist may sustain a higher breakdown

frequency, and cause losses to the company.

2. The action to be taken for the specific items or spare part is too general. Maintenance staff would

not have a clear idea with the general instruction. Therefore, a clear maintenance action should be

stated in the checklist in order to make sure the checking is complete for the relative part.

Advanced Materials Research Vol. 748 1209

3. Only assigned a ‘tick’ to the maintenance action after the maintenance action is done or probably

has not done yet, and intends to pass to the other personnel to make the repair. The ‘tick’ symbol

could not define the machine’s item or spare part condition clearly, and what is next action should

be taken or who is the next personal should handle the problem, it can be machine’s specialist, or

machine’s vendor.

Phase III Development of Effective Preventive Maintenance (PM) Checklist. Based on problems

identified in Phase II, discussions are conducted with maintenance engineers and technicians as well

as segregating all parts at the machine into two categories which is critical parts and non-critical parts.

Then, the PM checklist is restructured as shown in Fig.2. Besides that, some updating issues can be

added in the new checklist as well. For example, based on the root cause of the spare part’s failure,

maintenance personnel can state it in the remark column of the PM checklist, so that maintenance

personnel and operator can pay more attention and keep their eyes on the root cause of failures and

prevent it from occurring.

PREVENTIVE MAINTENANCE CHECKLIST

Equipment:

Equipment no:

Location:

Month:

SYMBOL

√

X REPAIR REQUIRED

R REPAIR ADJUSTED

O NOT APPLICABLE

Items Maintenance Action Duration (date) Remarks

Weeks

1 2 3 4

Critical items

Motor i. Check all motors

Pump

i. Check condition of all Hollmuller

pumps

ii. Check condition of all Assomma pumps

iii. Check all chemical pumps

Conveyor gear i. Check the condition of roller and chain

Non-critical

items

Piping i. Check all piping for any leakage

ii. Check CDA piping

Nozzle i. Change if nozzle broken

ii. Properly fixed after change

Filter or strainer

i. Check condition for all filters and

strainer

ii. No leaking after fixed

iii. Check exhaust scrubber ducting

iv. Check rinsing filter, change if any

corrosion

Main panel box i. Check the parameter shown in panel box

ii. Check aqua control

Oscillators i. If the oscillator malfunction, change it

Done by:_____________________ Date: ______________

(Maintenance personnel)

Verified by:___________________ Date: ______________

(Engineer/Supervisor)

Figure 2: New and Effective Preventive Maintenance Checklist

1210 Material and Manufacturing Technology IV

Summary

In the case study company, ineffectiveness maintenance operations improperly conduct of PM

checklist have led to the study of current preventive maintenance checklist. Based on the current and

new PM checklist, the predetermine interval for PM is only for weekly and monthly. It is because

weekly maintenance is for normal PM, whereas, monthly is for major PM. The new PM checklist can

be implemented by other machines if the steps of developing the new PM checklist are followed.

Consequently, the maintenance personnel workload can be reduced and also can concentrate to focus

to other machines as well. Most importantly, PM checklist should be updated from time to time for

continuous improvement. Table 1 shows the differences between applying the current PM checklist

and the new PM checklist.

Table 1: Differences between current and new PM checklist

Criteria Current PM Checklist New PM Checklist

The way to

conduct the

maintenance

actions

Simply ‘tick’ for all

maintenance actions in the

checklist

Use appropriate ‘symbol’ to check all the

maintenance operations so it can see clearly

what supposed to do with the condition of

components

Maintenance

task/instructions

Maintenance actions are

illustrated too general

Maintenance actions are illustrated

specifically for each items, so it an ease the

technicians work

Maintenance

schedule

No specific maintenance

duration, just only has weekly

and monthly.

Not all items are going to be checked every

week, depends on the criticality of parts to

be checked with certain period of schedule.

Inspection items

No segregation for all items Segregated into critical and non-critical

items to understand how to conduct PM

checklist

References

[1] E.H. Aghezzaf and N.M. Najib: Information Sciences Vol. 178 (2008), p. 3382-3392

[2] H. Ab-Samat, S. Kamaruddin and I.A. Azid: Journal of Science & Technology Pertanika Vol.19,

No.2, (2011), p.199-211.

[3] A.H.C. Tsang: Journal of Quality on Maintenance Vol. 4, (1998), p.87-94.

[4] K. Das, R.S. Laskari and S. Sengupta: European Journal of Operational Research Vol.183

(2007), p.162-180.

[5] N.T. Balakrishnan: Proceedings of Annual Reliability and Maintainability Symposium (1992),

p.109-118.

[6] C.M.F. Lapa, C.M.N.A Pereira and P.D.B. Marico: Reliability Engineering & System Safety Vol.

91 (2005), p.233-240.

Advanced Materials Research Vol. 748 1211

A Methodology for Evaluating and Scheduling Preventive Maintenance for a Thermo-Electric Unit Using Artificial Intelligence

Article

Full-text available

Mar 2023

Flow-production systems whose pieces are connected in a row may not have maintenance scheduling procedures fixed because problems occur at different times (electricity plants, cement plants, water desalination plants). Contemporary software and artificial intelligence (AI) technologies are used to fulfill the research objectives by developing a predictive maintenance program. The data of the fifth thermal unit of the power station for the electricity of Al Dora/Baghdad are used in this study. Three stages of research were conducted. First, missing data without temporal sequences were processed. The data were filled using time series hour after hour and the times were filled as system working hours, making the volume of the data relatively high for 2015-2016-2017. 2018 was utilized as a test year to assess the modeling work and validate the experimental results. In the second step, the artificial neural networks approach employs the python program as an AI, and the affinity ratio of real data using the performance measurement of the mean absolute error (MAE) was 0.005. To improve and reduce the value of absolute error, the genetic algorithm uses the python program and the convergence ratio became 0.001. It inferred that the algorithm is efficient in improving results. Thus, the genetic algorithm provided better results with fewer errors than the neural network alone. This concludes that the shown network has superior performance over others and the possibility of its long-term predictions for 2030. A Sing time series helped detect future cases by reading and inferring system data. The development of appropriate work plans will lower internal and external expenses of the systems and help integrate other capabilities by giving correct data sources of raw materials, costs, etc. To facilitate prediction for maintenance workers, an interface has been created that facilitates users to apply them using the python program represented by entering the times, an hour, a day, a month, a year, to predict the type and place of failure.

Maintenance Assessment of Power Plant using Artificial Intelligence

Thesis

Full-text available

Jan 2023

Abstract The systems that operate with the flowing production system which connected their parts are connected, respectively, makes the maintenance schedule is unstable, because damages occur at different times, and this is considered one of the main problems facing industrial systems. For example (power plants, cement factories, and water desalination plants. For this reason, the process of preventive maintenance based on prediction and scheduling is difficult and important. For most researchers, programs for maintenance are built on a probabilistic basis, through availability, reliability, and other indicators. This thesis proposed a methodology, predicting the failure, its type, and place of occurrence and time of occurrence, for a continuous production system for a unit of thermal electric energy produced. The previous and documented failure times, the hour of occurrence, the time taken to restore the system to work, and the type and location of failure. The thesis methodology was built on the basis of three stages. the first stage included the classification, arrangement, coding, and complement of data for uncomfortable times for the purpose of continuing the timing chain of times and compensation hour after hour for the operating times of the system in order to form a successive series that entered the data, which made the size of the data very large for the years 2015-2016-2017 and it has been used the year 2018 as a test year to verify modeling and verify the authenticity of experimental results. In the second stage, a methodology was implemented by Artificial Neural Networks(ANN), through the use of Python, depending on the results of the training to reach the best percentage of approchement with real data by using , Mean Absolute Error (MAE) performance index (0.005).To improve the weights of the nerve network, the Genetic Algorithm(GA), was used, as the methodology used (Ann with GA) improved the percentage of rapprochement with real data by using MAE performance measurement index (0.001), and it has shown that the proposed methodology exceeds the artificial nervous networks alone in reaching the best results. The third stage, which is the stage of prediction and evaluation, where a comparison was conducted between the real results and the data predicted by ANN once, and the ANN and GA, where the proposed methodology was closer to reality, as well as the performance measurement index. Finally, a predicting program was designed to replace the type, time and location of failure for subsequent years 2019-2030, and it gave good gambling results that benefit decision makers in determining the type of failure, and reducing the times of stopping, which reduces the cost through facades built using the Python language represented by inserting an hour-day-month-year to predict the faults, type and location of the faults. The proposed methodology can be applied to all systems with continuous production, and can be applied to intermittent production. It also gives flexibility to maintenance management, in predicting the time, type and location of failure, and thus scheduling, preventive maintenance, through which specialized cadres can be prepared to perform maintenance. As well as the necessary spare materials. And thus reduce downtime by making corrections to the convergent parts of the time of occurrence. It increases productivity and reduces costs.

Maintenance Performance Measurement: A Review

Article

Jul 2011

Maintenance is a vital system in a manufacturing company. The maintenance objectives are to ensure that the machine is in good condition, serviceable and safe to be operated in producing quality products. However, the system usually imposes a high cost system due to its ineffective activities. Therefore, this paper was written to review the factors affecting the maintenance effectiveness and methods used to measure its performance. In this paper, maintenance performance measurement methods are categorized into three groups, based on holistic, machine, and value factors. Each group is discussed based on the principles and techniques of the maintenance performance measurement methods, along with the example of its applications in the industry. The most common methods used are the holistic approach, overall equipment effectiveness (OEE), and balance score-card (BSC). These various methods have their own benefits and drawbacks, according to the area of measurement.

A Strategic approach to managing maintenance performance. J of Qual in Maint Eng 4(2): 87-94

Article

Jun 1998
J Qual Mainten Eng

Albert H.C. Tsang

Performance of the maintenance function is typically measured for operational control purposes. Since maintenance also has a strategic dimension, its performance measurement system should be linked to the espoused strategy of the function in order to get the maximum impact. This paper presents a structured approach to managing maintenance performance developed from this premise. The measurement system features a balanced scorecard (BSC) composed of key performance indicators (KPIs). The BSC is used to inform employees of the strategy pursued by the maintenance function, and to track the effectiveness of action plans in meeting the strategic objectives. The various steps involved in the process are also discussed.

Integrated Production Planning and Preventive Maintenance in Deteriorating Production Systems

Article

Sep 2008
INFORM SCIENCES

This paper discusses the issue of integrating production planning and preventive maintenance in manufacturing production systems. In particular, it tackles the problem of integrating production and preventive maintenance in a system composed of parallel failure-prone production lines. It is assumed that when a production line fails, a minimal repair is carried out to restore it to an ‘as-bad-as-old’ status. Preventive maintenance is carried out, periodically at the discretion of the decision maker, to restore the production line to an ‘as-good-as-new’ status. It is also assumed that any maintenance action, performed on a production line in a given period, reduces the available production capacity on the line during that period. The resulting integrated production and maintenance planning problem is modeled as a nonlinear mixed-integer program when each production line implements a cyclic preventive maintenance policy. When noncyclical preventive maintenance policies are allowed, the problem is modeled as a linear mixed-integer program. A Lagrangian-based heuristic procedure for the solution of the first planning model is proposed and discussed. Computational experiments are carried out to analyze the performance of the method for different failure rate distributions, and the obtained results are discussed in detail.

A model for preventive maintenance planning by genetic algorithms based in cost and reliability

Article

Feb 2006
RELIAB ENG SYST SAFE

This work has two important goals. The first one is to present a novel methodology for preventive maintenance policy evaluation based upon a cost-reliability model, which allows the use of flexible intervals between maintenance interventions. Such innovative features represents an advantage over the traditional methodologies as it allows a continuous fitting of the schedules in order to better deal with the components failure rates. The second goal is to automatically optimize the preventive maintenance policies, considering the proposed methodology for systems evaluation.Due to the great amount of parameters to be analyzed and their strong and non-linear interdependencies, the search for the optimum combination of these parameters is a very hard task when dealing with optimizations schedules. For these reasons, genetic algorithms (GA) may be an appropriate optimization technique to be used. The GA will search for the optimum maintenance policy considering several relevant features such as: (i) the probability of needing a repair (corrective maintenance), (ii) the cost of such repair, (iii) typical outage times, (iv) preventive maintenance costs, (v) the impact of the maintenance in the systems reliability as a whole, (vi) probability of imperfect maintenance, etc. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR was used as a case study. The results obtained by this methodology outline its good performance, allowing specific analysis on the weighting factors of the objective function.

Machine reliability and preventive maintenance planning for cellular manufacturing system

Article

Nov 2007
EUR J OPER RES

The paper proposes a preventive maintenance (PM) planning model for the performance improvement of cellular manufacturing systems (CMS) in terms of machine reliability, and resource utilization. In a CMS, parts are processed by a group of interdependent machines, where machine reliability plays an important role in the performance improvement of the cell. Assuming that machine failure times follow a Weibull distribution, the proposed model determines a PM interval and a schedule for performing PM actions on each machine in the cell by minimizing the total maintenance cost and the overall probability of machine failures. The model uses a combined cost and reliability based approach, and optimizes maintenance costs by administering a group maintenance policy subject to a desirable machine reliability threshold. The study also proposes a CMS design model that integrates the above PM concepts into the design process. Illustrative examples are presented to demonstrate the applicability of the proposed approach.

Jan 2008
3382-3392

E H Aghezzaf

E.H. Aghezzaf and N.M. Najib: Information Sciences Vol. 178 (2008), p. 3382-3392

Jan 2011
199-211

H Ab-Samat
S Kamaruddin
I A Azid

H. Ab-Samat, S. Kamaruddin and I.A. Azid: Journal of Science & Technology Pertanika Vol.19, No.2, (2011), p.199-211.

Jan 1998
87-94

A H C Tsang

A.H.C. Tsang: Journal of Quality on Maintenance Vol. 4, (1998), p.87-94.

Jan 2007
EUR J OPER RES
162-180

K Das
R S Laskari
S Sengupta

K. Das, R.S. Laskari and S. Sengupta: European Journal of Operational Research Vol.183 (2007), p.162-180.

Jan 2005
RELIAB ENG SYST SAFE
233-240

C M F Lapa
C M N Pereira
P D B Marico

C.M.F. Lapa, C.M.N.A Pereira and P.D.B. Marico: Reliability Engineering & System Safety Vol. 91 (2005), p.233-240.

Preventive Maintenance Checklist towards Effective Maintenance System: A Case Study in Semiconductor Industry

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