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

Advances in Manufacturing 2023年第2期11卷 248-263页

作者： Jon Martin Fordal Per SchjØlberg Hallvard Helgetun torøistein skjermo Yi Wang Chen Wang Department of Mechanical and Industrial Engineering Norwegian University of Science and Technology(NTNU)7491TrondheimNorway El-Watch AS 6657RindalNorway Business School University of BedfordshireLutonUK School of Mechanical Engineering Hubei University of Automotive TechnologyShiyan 442002HubeiPeople's Republic of China

Possessing an efficient production line relies heavily on the availability of the production ***,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 *** the emergence of advanced manufacturing processes,incorporating predictive maintenance capabilities is seen as a *** field of interest is how modern value chains can support the maintenance function in a *** to data from processes,equipment and products have increased significantly with the introduction of sensors and Industry 4.0 ***,how to gather and utilize these data for enabling improved decision making within maintenance and value chain is still a ***,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 *** research approach includes both theoretical testing and industrial *** paper presents a novel concept for a predictive maintenance platform,and an artificial neural network(ANN)model with sensor data ***,a case of a company that has chosen to apply the platform,with the implications and determinants of this decision,is also *** show that the platform can be used as an entry-level solution to enable Industry 4.0 and sensor data based predictive maintenance.

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

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