Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice

Enrico Zio (1, 2)

1 POLIMI - Politecnico di Milano [Milan]
2 CRC - Centre de recherche sur les Risques et les Crises

We are performing the digital transition of industry, living the 4th industrial revolution, building a new World in which the digital, physical and human dimensions are interrelated in complex socio-cyber-physical systems. For the sustainability of these transformations, knowledge, information and data must be integrated within model-based and data-driven approaches of Prognostics and Health Management (PHM) for the assessment and prediction of structures, systems and components (SSCs) evolutions and process behaviors, so as to allow anticipating failures and avoiding accidents, thus, aiming at improved safe and reliable design, operation and maintenance.There is already a plethora of methods available for many potential applications and more are being developed: yet, there are still a number of critical problems which impede full deployment of PHM and its benefits in practice. In this respect, this paper does not aim at providing a survey of existing works for an introduction to PHM nor at providing new tools or methods for its further development; rather, it aims at pointing out main challenges and directions of advancements, for full deployment of condition-based and predictive maintenance in practice.

Mots clés: Prognostics and Health Management (PHM); Predictive maintenance; Recurrent Neural Networks (RNNs); Reservoir Computing (RC); Generative Adversarial Networks (GANs); Deep Neural Networks (DNNs); Optimal Transport (OT)

Domaines: Sciences de l'ingénieur [physics]

Enrico Zio. Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering and System Safety, 2022, 218, pp.108119. ⟨10.1016/j.ress.2021.108119⟩. ⟨hal-03907690⟩