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001 978-981-16-9131-7
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007 cr nn 008mamaa
008 221019s2023 si | s |||| 0|eng d
020 _a9789811691317
_9978-981-16-9131-7
050 4 _aTA213-215
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_2bicssc
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_2bisacsh
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_2thema
082 0 4 _a621.8
_223
100 1 _aLei, Yaguo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aBig Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems
_h[electronic resource] /
_cby Yaguo Lei, Naipeng Li, Xiang Li.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXIII, 281 p. 116 illus., 104 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
500 _aAcceso multiusuario
505 0 _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.
520 _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.
541 _fUABC ;
_cPerpetuidad
650 0 _aMachinery.
650 1 4 _aMachinery and Machine Elements.
700 1 _aLi, Naipeng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aLi, Xiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811691300
776 0 8 _iPrinted edition:
_z9789811691324
776 0 8 _iPrinted edition:
_z9789811691331
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-16-9131-7
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c262636
_d262635