02687nam 22005655 450 991101587180332120250703130251.0981-9672-18-X10.1007/978-981-96-7218-9(MiAaPQ)EBC32195990(Au-PeEL)EBL32195990(CKB)39578306400041(DE-He213)978-981-96-7218-9(EXLCZ)993957830640004120250703d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierPrognostics and Health Management for Intelligent Electromechanical Systems /by Hui Liu, Fang Cheng, Yanfei Li1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (257 pages)981-9672-17-1 Introduction -- Feature extraction of bearing vibration signal -- Ensemble intelligent diagnosis for bearing faults -- Deep learning based prediction for bearing remaining useful life.-Optimization based prediction for IGBT remaining useful life.This book gives a detailed introduction to the technical background, feature extraction methods, PHM models and big data embedding methods of the big data theory in PHM for intelligent electromechanical systems. Combination with deep learning and big data, this book explains the hybrid algorithm framework of PHM such as ensemble intelligence and optimized intelligence and introduces PHM models for bearing, IGBT, MOSFET and other components and their big data embedding platform. This book improves the PHM method and theory of electromechanical system under industrial big data and provides reference for the development of intelligent electromechanical equipment and intelligent industrial production in the future. .MechatronicsComputational intelligenceEngineeringData processingMechatronicsComputational IntelligenceData EngineeringMechatronics.Computational intelligence.EngineeringData processing.Mechatronics.Computational Intelligence.Data Engineering.629.8Liu Hui274539Cheng Fang1833488Li Yanfei1827456MiAaPQMiAaPQMiAaPQBOOK9911015871803321Prognostics and Health Management for Intelligent Electromechanical Systems4408380UNINA