001431256 000__ 03955cam\a2200589Ii\4500
001431256 001__ 1431256
001431256 003__ OCoLC
001431256 005__ 20230308003228.0
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001431256 008__ 220115s2022\\\\si\a\\\\ob\\\\000\0\eng\d
001431256 019__ $$a1290840388$$a1291146279$$a1291170260$$a1294362343$$a1296666095
001431256 020__ $$a9789811680441$$q(electronic bk.)
001431256 020__ $$a9811680442$$q(electronic bk.)
001431256 020__ $$z9789811680434
001431256 020__ $$z9811680434
001431256 0247_ $$a10.1007/978-981-16-8044-1$$2doi
001431256 035__ $$aSP(OCoLC)1292353116
001431256 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dDCT$$dOCLCF$$dDKU$$dOCLCO$$dOCLCQ$$dYWS
001431256 049__ $$aISEA
001431256 050_4 $$aT57.5$$b.W36 2022
001431256 08204 $$a658.5$$223
001431256 1001_ $$aWang, Jing,$$eauthor.
001431256 24510 $$aData-driven fault detection and reasoning for industrial monitoring /$$cJing Wang, Jinglin Zhou, Xiaolu Chen.
001431256 264_1 $$aSingapore :$$bSpringer,$$c[2022]
001431256 264_4 $$c©2022
001431256 300__ $$a1 online resource (277 pages) :$$billustrations (chiefly color).
001431256 336__ $$atext$$btxt$$2rdacontent
001431256 337__ $$acomputer$$bc$$2rdamedia
001431256 338__ $$aonline resource$$bcr$$2rdacarrier
001431256 347__ $$atext file$$bPDF$$2rda
001431256 4901_ $$aIntelligent control and learning systems ;$$vvolume 3
001431256 504__ $$aIncludes bibliographical references.
001431256 5050_ $$aIntroduction -- Basic Statistical Fault Detection Problems -- Principal Component Analysis -- Canonical Variate Analysis -- Partial Least Squares Regression -- Fisher Discriminant Analysis -- Canonical Variate Analysis -- Fault Classification based on Local Linear Embedding -- Fault Classification based on Fisher Discriminant Analysis -- Quality-Related Global-Local Partial Least Square Projection Monitoring -- Locality-Preserving Partial Least-Squares Statistical Quality Monitoring -- Locally Linear Embedding Orthogonal Projection to Latent Structure (LLEPLS) -- Bayesian Causal Network for Discrete Systems -- Probability Causal Network for Continuous Systems -- Dual Robustness Projection to Latent Structure Method based on the L_1 Norm.
001431256 5060_ $$aOpen access$$5GW5XE
001431256 520__ $$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.
001431256 588__ $$aDescription based upon print version of record.
001431256 650_0 $$aIndustrial engineering$$xData processing.
001431256 650_0 $$aFault location (Engineering)$$xData processing.
001431256 650_6 $$aGénie industriel$$xInformatique.
001431256 650_6 $$aDétection de défaut (Ingénierie)$$xInformatique.
001431256 655_0 $$aElectronic books.
001431256 7001_ $$aZhou, Jinglin,$$eauthor.
001431256 7001_ $$aChen, Xiaolu,$$eauthor.
001431256 77608 $$iPrint version:$$aWang, Jing$$tData-Driven Fault Detection and Reasoning for Industrial Monitoring$$dSingapore : Springer Singapore Pte. Limited,c2022$$z9789811680434
001431256 830_0 $$aIntelligent control and learning systems ;$$vvolume 3.
001431256 852__ $$bebk
001431256 85640 $$3Springer Nature$$uhttps://link.springer.com/10.1007/978-981-16-8044-1$$zOnline Access$$91397441.2
001431256 909CO $$ooai:library.usi.edu:1431256$$pGLOBAL_SET
001431256 980__ $$aBIB
001431256 980__ $$aEBOOK
001431256 982__ $$aEbook
001431256 983__ $$aOnline
001431256 994__ $$a92$$bISE