Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems
- Indbinding:
- Hardback
- Sideantal:
- 296
- Udgivet:
- 20. oktober 2022
- Udgave:
- 22001
- Størrelse:
- 160x22x241 mm.
- Vægt:
- 612 g.
- 8-11 hverdage.
- 20. november 2024
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- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
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
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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