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dc.contributor.authorWang, Jing
dc.contributor.authorZhou, Jinglin
dc.contributor.authorChen, Xiaolu
dc.date.accessioned2022-01-14T13:41:53Z
dc.date.available2022-01-14T13:41:53Z
dc.date.issued2022
dc.identifierONIX_20220114_9789811680441_39
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/52452
dc.description.abstractThis 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. This is an open access book.
dc.languageEnglish
dc.relation.ispartofseriesIntelligent Control and Learning Systems
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TJ Electronics & communications engineering::TJF Electronics engineering::TJFM Automatic control engineering::TJFM1 Robotics
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence
dc.subject.otherMultivariate causality analysis
dc.subject.otherProcess monitoring
dc.subject.otherManifold learning
dc.subject.otherFault diagnosis
dc.subject.otherData modeling
dc.subject.otherFault classification
dc.subject.otherFault reasoning
dc.subject.otherCausal network
dc.subject.otherProbabilistic graphical model
dc.subject.otherData-driven methods
dc.subject.otherIndustrial monitoring
dc.subject.otherOpen Access
dc.titleData-Driven Fault Detection and Reasoning for Industrial Monitoring
dc.typebook
oapen.identifier.doi10.1007/978-981-16-8044-1
oapen.relation.isPublishedBy6c6992af-b843-4f46-859c-f6e9998e40d5
oapen.relation.isbn9789811680441
oapen.imprintSpringer Singapore
oapen.series.number3
oapen.pages264


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