LEADER 02893nam  22005295  450 
001 9910627272303321
005 20251009072000.0
010   $a981-16-9131-2
024 7 $a10.1007/978-981-16-9131-7
035   $a(MiAaPQ)EBC7119944
035   $a(Au-PeEL)EBL7119944
035   $a(CKB)25179516100041
035   $a(PPN)265860598
035   $a(DE-He213)978-981-16-9131-7
035   $a(OCoLC)1493008948
035   $a(EXLCZ)9925179516100041
100   $a20221019d2023      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBig Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems /$fby Yaguo Lei, Naipeng Li, Xiang Li
205   $a1st ed. 2023.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023.
215   $a1 online resource (292 pages)
225 1 $aEngineering Series
311 08$aPrint version: Lei, Yaguo Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems Singapore : Springer,c2022 9789811691300 
320   $aIncludes bibliographical references.
327   $aIntroduction 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.
330   $aThis book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies.
410  0$aEngineering Series
606   $aMachinery
606   $aMachinery and Machine Elements
615  0$aMachinery.
615 14$aMachinery and Machine Elements.
676   $a005.7
700   $aLei$b Yaguo$0983733
702   $aLi$b Naipeng
702   $aLi$b Xiang
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910627272303321
996   $aBig-data driven intelligent fault diagnosis and prognosis for mechanical systems$93058476
997   $aUNINA