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003 | TR-AnTOB | ||
005 | 20231116085138.0 | ||
007 | cr nn 008mamaa | ||
008 | 220103s2022 si | s |||| 0|eng d | ||
020 | _a9789811680441 | ||
024 | 7 |
_a10.1007/978-981-16-8044-1 _2doi |
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040 |
_aTR-AnTOB _beng _erda _cTR-AnTOB |
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041 | _aeng | ||
050 | 4 | _aT59.5 | |
072 | 7 |
_aTBM _2bicssc |
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072 | 7 |
_aTEC037000 _2bisacsh |
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072 | 7 |
_aTBM _2thema |
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090 | _aT59.5EBK | ||
100 | 1 |
_aWang, Jing. _eauthor. _0(orcid)0000-0002-6847-8452 _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aData-Driven Fault Detection and Reasoning for Industrial Monitoring _h[electronic resource] / _cby Jing Wang, Jinglin Zhou, Xiaolu Chen. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022. |
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300 | _a1 online resource | ||
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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490 | 1 |
_aIntelligent Control and Learning Systems, _x2662-5466 ; _v3 |
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505 | 0 | _aIntroduction -- Basic Statistical Fault Detection Problems -- Principal Component Analysis -- Canonical Variate Analysis -- Partial Least Squares Regression -- Fisher Discriminant Analysis -- Canonical Variate Analysis -- Fault Classification based on Local Linear Embedding -- Fault Classification based on Fisher Discriminant Analysis -- Quality-Related Global-Local Partial Least Square Projection Monitoring -- Locality-Preserving Partial Least-Squares Statistical Quality Monitoring -- Locally Linear Embedding Orthogonal Projection to Latent Structure (LLEPLS) -- Bayesian Causal Network for Discrete Systems -- Probability Causal Network for Continuous Systems -- Dual Robustness Projection to Latent Structure Method based on the L_1 Norm. | |
506 | 0 | _aOpen Access | |
520 | _aThis open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. | ||
650 | 0 | _aIndustrial engineering. | |
650 | 0 | _aAutomation. | |
650 | 0 | _aComputational intelligence. | |
650 | 1 | 4 | _aIndustrial Automation. |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aAutomation. |
653 | 0 | _aFault location (Engineering) -- Data processing | |
653 | 0 | _aIndustrial engineering -- Data processing | |
700 | 1 |
_aZhou, Jinglin. _eauthor. _0(orcid)0000-0003-1589-7423 _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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700 | 1 |
_aChen, Xiaolu. _eauthor. _0(orcid)0000-0002-4701-7235 _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
830 | 0 |
_aIntelligent Control and Learning Systems, _x2662-5466 ; _v3 |
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856 | 4 | 0 |
_uhttps://doi.org/10.1007/978-981-16-8044-1 _3Springer eBooks _zOnline access link to the resource |
942 |
_2lcc _cEBK |