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020 _a9789811680441
024 7 _a10.1007/978-981-16-8044-1
_2doi
040 _aTR-AnTOB
_beng
_erda
_cTR-AnTOB
041 _aeng
050 4 _aT59.5
072 7 _aTBM
_2bicssc
072 7 _aTEC037000
_2bisacsh
072 7 _aTBM
_2thema
090 _aT59.5EBK
100 1 _aWang, Jing.
_eauthor.
_0(orcid)0000-0002-6847-8452
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Control and Learning Systems,
_x2662-5466 ;
_v3
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
700 1 _aChen, Xiaolu.
_eauthor.
_0(orcid)0000-0002-4701-7235
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
830 0 _aIntelligent Control and Learning Systems,
_x2662-5466 ;
_v3
856 4 0 _uhttps://doi.org/10.1007/978-981-16-8044-1
_3Springer eBooks
_zOnline access link to the resource
942 _2lcc
_cEBK