Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0

doi:10.1007/s40436-022-00433-x

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (2): 248-263.doi: 10.1007/s40436-022-00433-x

• ARTICLES • 上一篇

Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0

Jon Martin Fordal¹, Per Schj?lberg¹, Hallvard Helgetun², Tor ?istein Skjermo², Yi Wang³, Chen Wang^3,4

1. Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway;
2. El-Watch AS, 6657, Rindal, Norway;
3. Business School, University of Bedfordshire, Luton, UK;
4. School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, Hubei, People's Republic of China

收稿日期:2022-03-09 修回日期:2022-05-04 发布日期:2023-05-20
通讯作者: Jon Martin Fordal,E-mail:jon.m.fordal@ntnu.no E-mail:jon.m.fordal@ntnu.no
作者简介:Jon Martin Fordal is a PhD Candidate at the Norwegian University of Science and Technology, Faculty of Engineering – Department of Mechanical and Industrial Engineering. He is currently working on a PhD with the title “Digitalization of the value chain – improving value chain performance with prediction.” Examples of research areas include maintenance management, digitalization and integration of maintenance and value chain, sensor management, and predictive maintenance. Prior to the PhD, he worked as a maintenance engineer in Elkem ASA. His main supervisor is Dr. Per Schj?lberg.
Per Schj?lberg is an Associate Professor and the former Head of the Production and Quality Engineering Department at the Norwegian University of Science and Technology, NTNU, Trondheim. He sits in the board of several organizations within the feld of maintenance.
Hallvard Helgetun is key account manager and has main responsibility for industrial projects and customers at El-Watch AS. He has worked with electronics for 15 years and collaborates with the development department on wireless sensor systems.
Tor ?istein Skjermo is chief technology ofcer and in charge of research and development at El-Watch AS. He has been working in the feld of wireless sensor systems for 20 years and holds several patents regarding practical usage and design of wireless sensors. He is also involved in improving the utilization of IoT sensors in industrial operation and maintenance.
Yi Wang obtained his Ph.D. from the Manufacturing Engineering Centre, Cardif University, UK, in 2008. He is an Associate professor in business decision making in the Faculty of Business, University of Plymouth, UK. Previously he worked in the Department of Computer Science, Southampton University, at the Business School, Nottingham Trent University, and in the School of Materials, University of Manchester. He holds various visiting lectureships in several universities worldwide. Dr Wang has special research interests in supply chain management, logistics, operation management, culture management, information systems, game theory, data analysis, semantics, and ontology analysis, and neuromarketing. Dr Wang has published 120 technical peerreviewed papers in international journals and conferences. He has coauthored two books: Operations Management for Business, Fashion Supply Chain and Logistics Management and Data Mining for Zerodefect Manufacturing.
Chen Wang is an associate professor and master supervisor of Hubei University of Automotive Industry. His main research interests are machine learning and intelligent manufacturing.
基金资助:
This study is supported by the research project Cyber Physical Systems in plant perspective (CPS-Plant). The Research Council of Norway is funding CPS-Plant. The authors are also grateful for contributions and support from the case company.

Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0

Jon Martin Fordal¹, Per Schj?lberg¹, Hallvard Helgetun², Tor ?istein Skjermo², Yi Wang³, Chen Wang^3,4

1. Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway;
2. El-Watch AS, 6657, Rindal, Norway;
3. Business School, University of Bedfordshire, Luton, UK;
4. School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, Hubei, People's Republic of China

Received:2022-03-09 Revised:2022-05-04 Published:2023-05-20
Contact: Jon Martin Fordal,E-mail:jon.m.fordal@ntnu.no E-mail:jon.m.fordal@ntnu.no
Supported by:
This study is supported by the research project Cyber Physical Systems in plant perspective (CPS-Plant). The Research Council of Norway is funding CPS-Plant. The authors are also grateful for contributions and support from the case company.

摘要/Abstract

摘要： Possessing an efficient production line relies heavily on the availability of the production equipment. Thus, to ensure that the required function for critical equipment is in compliance, and unplanned downtime is minimized, succeeding with the field of maintenance is essential for industrialists. With the emergence of advanced manufacturing processes, incorporating predictive maintenance capabilities is seen as a necessity. Another field of interest is how modern value chains can support the maintenance function in a company. Accessibility to data from processes, equipment and products have increased significantly with the introduction of sensors and Industry 4.0 technologies. However, how to gather and utilize these data for enabling improved decision making within maintenance and value chain is still a challenge. Thus, the aim of this paper is to investigate on how maintenance and value chain data can collectively be used to improve value chain performance through prediction. The research approach includes both theoretical testing and industrial testing. The paper presents a novel concept for a predictive maintenance platform, and an artificial neural network (ANN) model with sensor data input. Further, a case of a company that has chosen to apply the platform, with the implications and determinants of this decision, is also provided. Results show that the platform can be used as an entry-level solution to enable Industry 4.0 and sensor data based predictive maintenance.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00433-x

关键词: Predictive maintenance (PdM) platform, Industry 4.0, Value chain performance, Anomaly detection, Artificial neural networks (ANN)

Abstract: Possessing an efficient production line relies heavily on the availability of the production equipment. Thus, to ensure that the required function for critical equipment is in compliance, and unplanned downtime is minimized, succeeding with the field of maintenance is essential for industrialists. With the emergence of advanced manufacturing processes, incorporating predictive maintenance capabilities is seen as a necessity. Another field of interest is how modern value chains can support the maintenance function in a company. Accessibility to data from processes, equipment and products have increased significantly with the introduction of sensors and Industry 4.0 technologies. However, how to gather and utilize these data for enabling improved decision making within maintenance and value chain is still a challenge. Thus, the aim of this paper is to investigate on how maintenance and value chain data can collectively be used to improve value chain performance through prediction. The research approach includes both theoretical testing and industrial testing. The paper presents a novel concept for a predictive maintenance platform, and an artificial neural network (ANN) model with sensor data input. Further, a case of a company that has chosen to apply the platform, with the implications and determinants of this decision, is also provided. Results show that the platform can be used as an entry-level solution to enable Industry 4.0 and sensor data based predictive maintenance.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00433-x

Key words: Predictive maintenance (PdM) platform, Industry 4.0, Value chain performance, Anomaly detection, Artificial neural networks (ANN)

Jon Martin Fordal, Per Schj?lberg, Hallvard Helgetun, Tor ?istein Skjermo, Yi Wang, Chen Wang. Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0[J]. Advances in Manufacturing, 2023, 11(2): 248-263.

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Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0

Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0

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