INTELLIGENT FAULT DIAGNOSIS AND PROGNOSIS FOR INDUSTRIAL SYSTEMS: CROSS-DOMAIN, ZERO-SAMPLE, AND DEGRADATION MODELING METHODS
Author:   Hongpeng Yin, PhD (Chongqing University, China) ,  Li Cai, B.E. (Chongqing University, China) ,  Peng Zhang (Chongqing University, China)
Publisher:   Elsevier - Health Sciences Division
ISBN:  

9780443442919


Pages:   222
Publication Date:   20 February 2026
Format:   Paperback
Availability:   Not yet available   Availability explained
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INTELLIGENT FAULT DIAGNOSIS AND PROGNOSIS FOR INDUSTRIAL SYSTEMS: CROSS-DOMAIN, ZERO-SAMPLE, AND DEGRADATION MODELING METHODS


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Author:   Hongpeng Yin, PhD (Chongqing University, China) ,  Li Cai, B.E. (Chongqing University, China) ,  Peng Zhang (Chongqing University, China)
Publisher:   Elsevier - Health Sciences Division
Imprint:   Elsevier - Health Sciences Division
Weight:   0.450kg
ISBN:  

9780443442919


ISBN 10:   0443442916
Pages:   222
Publication Date:   20 February 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

1. Introduction 2. Basic theories and methods of intelligent fault diagnosis and health prediction 3. Multi-attribute learning framework for zero-sample fault detection in machinery 4. Generalized zero-sample industrial fault diagnosis with domain bias 5. Generalized zero-sample industrial fault diagnosis under cross-domain scenarios 6. Learning across multisource domains for generalized zero-sample industrial fault diagnosis 7. Federated generalized zero-sample industrial fault diagnosis across multisource domains 8. A multi-phase Wiener process-based degradation model with imperfect maintenance activities

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Author Information

Professor Hongpeng Yin is based at the School of Automation, Chongqing University in China. His current research interests mainly include data-driven process monitoring and fault diagnosis, pattern recognition, and data mining Li Cai received the B.E. degree from the School of Physics and Electronic Engineering from Hainan Normal University in 2019. He is currently undertaking a Ph.D. degree at the School of Automation, Chongqing University, China. His major research interests include data-driven fault detection and diagnosis, fault prediction, remaining useful life prediction, and (generalized) zero-shot learning Peng Zhang received the B.E. degree from College of Automation, Hangzhou Dianzi University, China in 2021. He is currently working towards a Ph.D. degree in the College of Automation, Chongqing University, China. His research interests include data mining, fault diagnosis and machine learning

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