00732nam0-22002531i-450-990001150140403321000115014FED01000115014(Aleph)000115014FED0100011501420000920d1977----km-y0itay50------baengORDINARY and delay differential equationsby DriverNew York [etc.]Springer-Verlag1977Applied mathematical sciences20Driver,Rodney David46404ITUNINARICAUNIMARCBK990001150140403321C-33-(2018862MA1MA1ORDINARY and delay differential equations346429UNINAING0103083nam 22006733 450 991052009900332120250628110032.0981-16-8044-2(CKB)5340000000068900(MiAaPQ)EBC6840160(Au-PeEL)EBL6840160(OCoLC)1292353116(oapen)https://directory.doabooks.org/handle/20.500.12854/77320(PPN)262175452(ODN)ODN0010067830(oapen)doab77320(EXLCZ)99534000000006890020220207d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierData-Driven Fault Detection and Reasoning for Industrial MonitoringSpringer Nature2022Singapore :Springer Singapore Pte. Limited,2022.©2022.1 online resource (277 pages)Intelligent Control and Learning Systems ;v.3981-16-8043-4 This 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.Intelligent Control and Learning SystemsRoboticsbicsscArtificial intelligencebicsscMultivariate causality analysisProcess monitoringManifold learningFault diagnosisData modelingFault classificationFault reasoningCausal networkProbabilistic graphical modelData-driven methodsIndustrial monitoringOpen AccessRoboticsArtificial intelligenceTEC009000TEC037000bisacshWang Jing1974 April 21-1380923Zhou Jinglin1076407Chen Xiaolu1076408MiAaPQMiAaPQMiAaPQBOOK9910520099003321Data-Driven Fault Detection and Reasoning for Industrial Monitoring3423101UNINA