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Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems - Xiang Li - 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 presentPresents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosisProvides abundant experimental validations and engineering cases of the presented methodologies

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9789811691300
  • Indbinding:
  • Hardback
  • Sideantal:
  • 296
  • Udgivet:
  • 20. oktober 2022
  • Udgave:
  • 22001
  • Størrelse:
  • 160x22x241 mm.
  • Vægt:
  • 612 g.
  • 8-11 hverdage.
  • 6. december 2024
På lager

Normalpris

  • BLACK NOVEMBER

Medlemspris

Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

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 presentPresents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosisProvides 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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