Librería:
Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Vendedor de AbeBooks desde 22 de noviembre de 2018
pp. 296. N° de ref. del artículo 26398550106
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:
Acerca del autor:
Yaguo Lei is a full professor in School of Mechanical Engineering at Xi’an Jiaotong University (XJTU), P. R. China, which he joined as an associate professor in 2010. Prior to that, he worked at the University of Alberta, Canada, as a postdoctoral research fellow. He ever worked at the University of Duisburg-Essen, Germany, as an Alexander von Humboldt fellow in 2012. He was promoted to full professor in 2013. He received the B.S. and the Ph.D. degrees both in Mechanical Engineering from XJTU, in 2002 and 2007, respectively. He is an associate editor or a member of the editorial boards of more than ten journals, including IEEE Transactions on Industrial Electronics, Mechanical Systems and Signal Processing, Measurement Science & Technology, and Neural Computing & Applications. He is also a Fellow of the Institution of Engineering and Technology (IET), a Fellow of the International Society of Engineering Asset Management (ISEAM), a senior member of IEEE and a member of ASME, respectively. He has pioneered many signal processing techniques, intelligent fault diagnosis methods, and remaining useful life prediction models for mechanical systems.
Naipeng Li is currently an assistant professor in School of Mechanical Engineering at Xi’an Jiaotong University, P. R. China. He received the B.S. degree in Mechanical Engineering from Shandong Agricultural University, P. R. China, in 2012, and the Ph.D. degree in Mechanical Engineering from Xi'an Jiaotong University, P. R. China, in 2019. He was also a visiting scholar of Georgia Institute of Technology, Atlanta, USA, from 2016 to 2018. His research interests include condition monitoring, intelligent fault diagnostics, and RUL prediction of rotating machinery.
Xiang Li is currently an associate professor in School of Mechanical Engineering at Xi’an Jiaotong University, P. R. China. He received the B.S. and Ph.D. degrees both in Mechanics from Tianjin University, P. R. China, in 2012 and 2017, respectively. Prior to joining Xi’an Jiaotong University, he was a postdoctoral fellow in Intelligent Maintenance Systems Center at University of Cincinnati, USA, and an associate professor at Northeastern University, P. R. China. He was also a visiting scholar in School of Engineering at University of California, Merced, USA, from 2015 to 2016. His research interests include industrial artificial intelligence, industrial big data, and machinery fault diagnosis and prognosis. He is an early career advisory board member of IEEE/CAA Journal of Automatica Sinica, and an editor of three international journals.
Título: Big Data-Driven Intelligent Fault Diagnosis ...
Editorial: Springer
Año de publicación: 2023
Encuadernación: Encuadernación de tapa blanda
Condición: New
Librería: WeBuyBooks, Rossendale, LANCS, Reino Unido
Condición: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned. Nº de ref. del artículo: wbs9461185595
Cantidad disponible: 1 disponibles
Librería: moluna, Greven, Alemania
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides basic theories and detailed background for fault diagnosis and prognosisCovers state-of-the-art techniques and advancements in the field of intelligent fault diagnosis and RUL predictionProvides abundant experimental and industrial. Nº de ref. del artículo: 1128556471
Cantidad disponible: Más de 20 disponibles
Librería: preigu, Osnabrück, Alemania
Taschenbuch. Condición: Neu. Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems | Yaguo Lei (u. a.) | Taschenbuch | xiii | Englisch | 2023 | Springer | EAN 9789811691331 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 127796495
Cantidad disponible: 5 disponibles
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Condición: New. In English. Nº de ref. del artículo: ria9789811691331_new
Cantidad disponible: Más de 20 disponibles
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Taschenbuch. Condición: Neu. Neuware -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 methodologiesSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch. Nº de ref. del artículo: 9789811691331
Cantidad disponible: 2 disponibles
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 296 pp. Englisch. Nº de ref. del artículo: 9789811691331
Cantidad disponible: 2 disponibles
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9789811691331
Cantidad disponible: 1 disponibles
Librería: Biblios, Frankfurt am main, HESSE, Alemania
Condición: New. PRINT ON DEMAND pp. 296. Nº de ref. del artículo: 18398550096
Cantidad disponible: 4 disponibles
Librería: Revaluation Books, Exeter, Reino Unido
Paperback. Condición: Brand New. 294 pages. 9.25x6.10x0.62 inches. In Stock. Nº de ref. del artículo: x-9811691339
Cantidad disponible: 2 disponibles