Intelligent Fault Diagnosis and Prognosis for Industrial Systems: Cross-Domain, Zero-Sample, and Degradation Modeling Methods

Name: Intelligent Fault Diagnosis and Prognosis for Industrial Systems: Cross-Domain, Zero-Sample, and Degradation Modeling Methods
Brand: Elsevier
SKU: 9780443442919
Price: 8405.0 INR
Availability: InStock

by Hongpeng Yin , Li Cai , Peng Zhang

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ISBN13: 9780443442919
Binding: Paperback
Subject: N/A
Publisher: Elsevier
Publisher Imprint: Elsevier
Publication Date: February 6, 2026
Pages: 222
Original Price: USD 145.0
Language: English
Edition: N/A
Item Weight: 450 grams
BISAC Subject(s): Engineering (General)

About The Book

Industrial Fault Diagnosis and Remaining Useful Life Prediction: Cross-Domain, Zero-Sample, and Degradation Modeling Methods introduces zero-sample learning methods that enable fault diagnosis and Predict Remaining Useful Life (RUL) without the need for labelled fault data. This is particularly valuable in industrial settings where labelled data is scarce or unavailable. Offers step-by-step guidance on implementing zero-shot learning models using real industrial data, reducing the learning curve for practitioners; includes real-world industrial case studies to demonstrate the application of zero-sample learning techniques in various industries, such as manufacturing, energy, and transportation. Such case studies provide readers with actionable insights and practical solutions. The book covers advanced methodologies for predicting the remaining useful life of industrial equipment, supporting readers in optimizing maintenance schedules, reducing downtime and extending the lifespan of critical assets. Covers state-of-the-art algorithms, including deep learning, transfer learning and domain adaptation, tailored for zero-sample scenarios. These tools empower readers to develop robust fault diagnosis and RUL prediction systems, enhancing predictive maintenance capabilities and ensuring the reliability of industrial systems.

About The Author

Cai, Li: - 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

Yin, Hongpeng: -

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

Zhang, Peng: - 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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