Predictive maintenance : technologies and methods

In a constantly changing industrial context, maintenance plays a key role in the performance and competitiveness of companies. The rise of digital technologies and Industry 4.0 has profoundly transformed asset management strategies, giving rise to maintenance. This approach relies on advanced data analysis and artificial intelligence to anticipate failures and optimize equipment management.

The first section describes the origins and challenges of predictive maintenance. It discusses the background to its emergence since the early 20th century, the growing importance of predictive management of industrial assets, and the challenges associated with its deployment, particularly in terms of cost, technological integration and positive return on investment. The second section highlights the need to assess the criticality of the equipment that will be the focus of predictive maintenance. It presents the different methods of assessing criticality to help decision-making. The third section proposes a classification of failures, distinguishing between random and probabilistic failures, and clarifying the notion of root cause. It clarifies the differences between random and deterministic failures. The fourth section takes a closer look at the probabilistic characterization of failures, integrating material uncertainties and using probability laws to estimate equipment service life, analyze health characteristics and calculate RUL. The fifth section presents the importance of defining alarm thresholds in predictive maintenance to minimize detection errors. It describes statistical and advanced methods, such as RUL integration, equipment health, machine learning and Bayesian approaches. The sixth section details decision methods in predictive maintenance, to minimize false alarms and non-detections. Binary hypothesis testing, statistical and Bayesian approaches, and machine learning techniques for dynamically adjusting decision thresholds are described. The seventh section succinctly describes the technologies and data types used, as well as associated signal processing techniques, the role of the Internet of Things (IoT), smart sensors and big data. The eighth section provides an inventory of the limitations of remaining useful life (RUL) estimation methods, and highlights the challenges associated with model accuracy, variability of operational conditions and uncertainty management. The ninth section presents concrete examples of predictive maintenance applications in various industries (aeronautics, rail, automotive) and the benefits obtained. The tenth section provides a retrospective of feedback from companies, highlighting the advantages and disadvantages according to company size (large corporations, ETIs and SMEs). Finally, the eleventh section looks at the future prospects for predictive...