KEY CONCEPTS OF MAINTENANCE IN INDUSTRY 4.0

Abstract

Industry 4.0 is a name given to the current trend of automation and data exchange in industrial technologies. It includes the Industrial Internet of things (IIoT), wireless sensors, cloud computing, artificial intelligence (AI) and machine learning. Industry 4.0 is commonly referred to as the fourth industrial revolution. An important thing in Industry 4.0 is Maintenance 4.0.

Full text

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Concepts, Traditional Maintenance 1.0, Maintenance 4.0, Industry 4.0, Transformation
Miroslav FUSKO, Monika BUČKOVÁ, Arkadiusz GOLA 
KEY CONCEPTS OF MAINTENANCE IN INDUSTRY 4.0
Abstract
Industry 4.0 is a name given to the current trend of automation and data exchange in industrial
technologies. It includes the Industrial Internet of things (IIoT), wireless sensors, cloud
computing, artificial intelligence (AI) and machine learning. Industry 4.0 is commonly referred
to as the fourth industrial revolution. An important thing in Industry 4.0 is Maintenance 4.0.
Maintenance 4.0 combines Machine Learning based Predictive Maintenance, Automation of
Failure Reporting, Scheduling, Parts Management etc. and Robotics/Drone Assisted Repair.
1. MAINTENANCE 4.0
In the current manufacturing world, the role of maintenance has been receiving increasingly more
attention while companies understand that maintenance, when well performed, can be a strategic
factor to achieve corporate goals. The latest trends of maintenance leans towards the predictive
approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based
Maintenance (CBM) techniques. The implementation of such approaches demands a well-structured
conception and can be boosted through the use of emergent ICT technologies, namely the Internet
of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore,
this paper describes an approach of an intelligent and predictive maintenance system, aligned with
Industry 4.0 principles, that considers an advanced and online analysis of the collected data for
the earlier detection of the occurrence of possible machine failures, and supports technicians during
the maintenance interventions by providing a guided intelligent decision support. [1], [2]
Maintenance 4.0 is a machine-assisted digital version of all the things we have been doing for
the past forty years as humans to ensure our assets deliver value for our organization.
Maintenance 4.0 includes a holistic view of sources of data, ways to connect, ways to collect,
 Miroslav Fusko, Ing., PhD. Department of Industrial Engineering, Faculty of Mechanical Engineering,
University of Žilina, Univerzitná 1, 010 26 Žilina, Slovak Republic, miroslav.fusko@fstroj.uniza.sk
 Monika Bučková, Ing., PhD. Department of Industrial Engineering, Faculty of Mechanical Engineering,
University of Žilina, Univerzitná 1, 010 26 Žilina, Slovak republic, monika.buckova@fstroj.uniza.sk
 Arkadiusz Gola, Politechnika Lubelska, ul. Nadbystrzycka 38 D, 20 – 618 Lublin, a.gola@pollub.pl

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ways to analyze and recommended actions to take in order to ensure asset function (reliability)
and value (asset management) are digitally assisted. For example, traditional Maintenance 1.0
includes sending highly trained specialists to collect machinery vibration analysis readings
on pumps, motors and gearboxes. Maintenance 4.0 includes a wireless vibration sensor
connected to a cloud server and machine learning platform to analyze the complex patterns
and provide automated service advice to the asset owner. With Maintenance 4.0, the vibration
specialist will no longer waste time going to the data; the data, when in need of subject matter
expert analysis, will go to the human. The decisions are what we call “digitally assisted” –
a partnership between man and machine. [3], [4], [5]
2. KEY CONCEPTS OF MAINTENANCE 4.0
We are in the midst of a transformation that started already a few years ago, nicknamed as the 4th
industrial revolution. According to Klaus Schwab of the World Economic Forum it’s all about
‘industrial convergence’. That is the merger between the physical, digital and biological world.
This is possible thanks to developments such as:
 robotization,
 nanotechnology,
 biotechnology,
 3D printing,
 virtual reality,
 Internet of Things.
When we focus the ‘4th industrial revolution’ on maintenance, we hear terms such as:
 Predictive Maintenance,
 IIoT (Industrial Internet of Things),
 Edge Computing.
The following nine terms update basic knowledge of Maintenance 4.0.:
 Preventive Maintenance
Preventive maintenance acts on the principle of ‘prevention are better than cure’. Instead
of waiting for a malfunction to occur, the intelligent software schedules a maintenance plan. The
goal is to prevent failures before they occur. This contrasts with the old approach of ‘Run to
failure’ maintenance which is reactive.
 Industrial Internet of Things
Industrial Internet of Things (IIoT) is one of the basic blocks of the 4th industrial revolution.
Engineers are linking more and more components, installations and objects. This enables new
analysis and insights. The most elaborated application today is predictive maintenance combined
with big data analysis.
 Predictive Maintenance
Predictive maintenance goes a step further than replacing a certain part after a fixed number
of running hours. The intelligent software looks at the part’s health based on ‘condition
monitoring’. Condition monitoring uses data from:
 vibration measurements,
 oil analysis,
 or infrared measurements.

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The intelligent software predicts if a failure is likely within a certain time frame. Engineers can
thus schedule and deliver maintenance better and decrease maintenance costs.
 Big Data Analytics
Monitoring your key installations delivers a large amount of data. This ‘Big Data’ contains
a wealth of information. It is possible to predict the ‘unpredictable’ when you link information
streams from inside and outside the company.
The PwC report ‘Mainnovation’ about Predictive Maintenance 4.0 shows this becoming reality.
Top companies provide continuous asset monitoring with warnings based on predictive
techniques. Regression analysis is one of those techniques.
 Cloud Computing versus Edge Computing
The evolution to store more and more data and to perform calculations in the cloud continues.
Both for individuals and companies. Yet there are good reasons in the industry for doing Edge
Computing. With Edge Computing your data remains close to its source for processing. It is
a method to optimize cloud computing by performing data processing near the source of the data.
It is the edge of the network. This is faster, safer and cheaper when you split between data stored
and processed locally, and data sent to the cloud. [6]
 Artificial Intelligence
Large internet companies pump billions in research and development in the field of artificial
intelligence. New breakthroughs follow each other faster and faster. The industry is working
with cobots. They are robots that collaborate with and learn from human colleagues. Inspection
drones and cleaning robots also start playing a larger role in the maintenance.
 Predictive Analytics
Predictive analytics encompasses a variety of statistical techniques from predictive modelling,
machine learning, and data mining that analyze current and historical facts to make predictions
about future or otherwise unknown events.
 Prognostic Maintenance
Prognostics is an engineering discipline focused on predicting the time at which a system
or a component will no longer perform its intended function. This form of maintenance builds
on predictive analytics and maintenance. It uses machine learning, pattern recognition, and other
advanced techniques like ‘neural networks’ and ‘neural fuzzy systems’.
 Prescriptive Maintenance and Analytics
The most advanced option in maintenance. Prescriptive maintenance tries to answer the question:
‘What should we do to achieve X?’. It’s based on:
 big data,
 graph analysis,
 simulations,
 complex event processing,
 neural networks,
 heuristics,
 machine learning.
Prescriptive goes a step further than predictive maintenance because it not only reflects
the possible results of a particular approach but also evaluates which approach is the fastest
or most efficient. [7]

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3. CONCLUSION
Revolutions are unpredictable and chaotic. [8] The adoption of Maintenance 4.0 has broad
implications for the industrial sector. We do not have to assume a crude ‘zero-sum game’ — that
for every winner there will be a corresponding loser. At the same time, many stakeholders will
struggle to adapt. While we do not expect disruption to occur overnight, some of the traditional
players are vulnerable and may not survive into the digitalization era.
This article was created with support of project: KEGA 017ŽU-4/2019
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