000 | 03102nam a22005175i 4500 | ||
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001 | 978-981-16-9131-7 | ||
003 | DE-He213 | ||
005 | 20240207153706.0 | ||
007 | cr nn 008mamaa | ||
008 | 221019s2023 si | s |||| 0|eng d | ||
020 |
_a9789811691317 _9978-981-16-9131-7 |
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050 | 4 | _aTA213-215 | |
072 | 7 |
_aTGB _2bicssc |
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_aTEC046000 _2bisacsh |
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_aTGB _2thema |
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082 | 0 | 4 |
_a621.8 _223 |
100 | 1 |
_aLei, Yaguo. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIII, 281 p. 116 illus., 104 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 |
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650 | 0 | _aMachinery. | |
650 | 1 | 4 | _aMachinery and Machine Elements. |
700 | 1 |
_aLi, Naipeng. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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 | ||
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