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This article explores predictive maintenance technologies and methods, addressing the challenges of implementation to ensure a return on investment. It examines the criteria for selecting critical equipment and the probabilistic nature of failures, emphasizing the key importance of estimating the Remaining Useful Life (RUL). Decision theory is then detailed to establish thresholds that prevent erroneous decisions. The technologies and data used are briefly presented, along with concrete examples and industry feedback on its advantages and limitations. Finally, the article concludes with perspectives on the future evolution of predictive maintenance.
This article introduces the concepts of intelligent predictive maintenance for industry 4.0, whose goal is to predict the moment of occurrence of a failure to implement appropriate actions to avoid it. It provides a description of the concepts of Industry 4.0, also referred to as the industry of the future, which emerged as part of the digital transformation of businesses. After a reminder of terminology, the methods, and tools essential for the design of this maintenance strategy are developed. Finally, a review of its current implementations in the various industries is proposed highlighting its advantages and disadvantages.
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