Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0
AffiliationNorwegian University of Science & Technology
El Watch AS
University of Bedfordshire
Hubei University of Automotive Technology
Subjectspredictive maintenance (PdM) platform
value chain performance
artificial neural networks (ANN)
Subject Categories::H710 Manufacturing Systems Engineering
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AbstractPossessing 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.
CitationFordal JM, Schjolberg P, Helgetun H, Skjermo TO, Wang Y, Wang C (2023) 'Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0', Advances in manufacturing, 11, pp.248-263.
JournalAdvances in manufacturing
SponsorsThis 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. Open access funding provided by NTNU Norwegian University of Science and Technology (incl St. Olavs Hospital - Trondheim University Hospital).
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