Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems / / by Yaguo Lei, Naipeng Li, Xiang Li

• Autore: Lei Yaguo
• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
• Edizione: 1st ed. 2023.
• Descrizione fisica: 1 online resource (292 pages)
• Disciplina: 005.7
• Soggetto topico: Machinery; Machinery and Machine Elements
• Persona (resp. second.): LiNaipeng; LiXiang
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Introduction and Background -- Traditional Intelligent Fault Diagnosis -- Hybrid Intelligent Fault Diagnosis Methods -- Deep Learning-Based Intelligent Fault Diagnosis -- Data-Driven RUL Prediction -- Data-Model Fusion RUL Prediction.
• Sommario/riassunto: 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.
• Titolo autorizzato: Big-data driven intelligent fault diagnosis and prognosis for mechanical systems
• ISBN: 981-16-9131-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910627272303321
• Lo trovi qui: Univ. Federico II
• Serie: Engineering Series