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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Veröffentlicht in:	Reliability engineering & system safety Jg. 218; S. 108119
1. Verfasser:	Zio, Enrico
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Barking Elsevier Ltd 01.02.2022 Elsevier BV Elsevier
Schlagworte:	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-Zugang:	Volltext
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