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

Af: Xiang Li, Yaguo Lei, Naipeng Li Engelsk Paperback

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

Af: Xiang Li, Yaguo Lei, Naipeng Li Engelsk Paperback
Tjek vores konkurrenters priser
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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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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 281
ISBN-13: 9789811691331
Indbinding: Paperback
Udgave:
ISBN-10: 9811691339
Kategori: Maskinteknik
Udg. Dato: 21 okt 2023
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 21 okt 2023
Forfatter(e): Xiang Li, Yaguo Lei, Naipeng Li
Forfatter(e) Xiang Li, Yaguo Lei, Naipeng Li

Kategori Maskinteknik

ISBN-13 9789811691331

Sprog Engelsk

Indbinding Paperback

Sider 281

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 21 okt 2023

Oplagsdato 21 okt 2023

Forlag Springer Verlag, Singapore

Kategori sammenhænge