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Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems
Hardcover
ISBN13: 9789811691300
Publisher: Springer Nature
Published: Oct 20 2022
Pages: 281
Weight: 1.30
Height: 0.69 Width: 6.14 Depth: 9.21
Language: English
This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era.
Features:
- Addresses the critical challenges in the field of PHM at present
- Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis
- Provides abundant experimental validations and engineering cases of the presented methodologies
1 different editions
Also available
Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems
Lei, Yaguo
Li, Naipeng
Li, Xiang
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Also from
Lei, Yaguo
Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems
Lei, Yaguo
Li, Naipeng
Li, Xiang
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Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
Lei, Yaguo
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