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

•Main PHM challenges in industry 4.0: physics, data and solution requirements.•Data challenges: missing of anomalies, labels and the continuously monitored data.•Advancing methods of detection, diagnostics and prognostics for data challenge.•Solution requirements challenge: interpretability, securit...

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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 218; p. 108119
Main Author: Zio, Enrico
Format: Journal Article
Language:English
Published: Barking Elsevier Ltd 01.02.2022
Elsevier BV
Elsevier
Subjects:
Cyber-physical systems
Deep Neural Networks (DNNs)
Engineering Sciences
Generative Adversarial Networks (GANs)
Optimal Transport (OT)
Predictive maintenance
Prognostics and Health Management (PHM)
Recurrent Neural Networks (RNNs), Reservoir Computing (RC)
Reliability engineering
Recurrent Neural Networks (RNNs), Reservoir Computing (RC)
Deep Neural Networks (DNNs)
Optimal Transport (OT)
Prognostics and Health Management (PHM)
Generative Adversarial Networks (GANs)
Predictive maintenance
Reservoir Computing (RC)
Recurrent Neural Networks (RNNs)
ISSN:0951-8320, 1879-0836
Online Access:Get full text
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Summary:•Main PHM challenges in industry 4.0: physics, data and solution requirements.•Data challenges: missing of anomalies, labels and the continuously monitored data.•Advancing methods of detection, diagnostics and prognostics for data challenge.•Solution requirements challenge: interpretability, security, uncertainty of models. 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.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2021.108119