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
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
.
DOI: 10.1007/s40436-022-00433-x
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