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Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems - Yaguo Lei - Bog

Bag om Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems

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

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9789811691331
  • Indbinding:
  • Paperback
  • Sideantal:
  • 296
  • Udgivet:
  • 21. Oktober 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 155x17x235 mm.
  • Vægt:
  • 452 g.
  • 2-3 uger.
  • 2. Juli 2024
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Beskrivelse af Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems

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

Brugerbedømmelser af Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems



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