001480932 000__ 03527cam\\22005417i\4500
001480932 001__ 1480932
001480932 003__ OCoLC
001480932 005__ 20231031003315.0
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001480932 007__ cr\un\nnnunnun
001480932 008__ 230919s2023\\\\si\a\\\\ob\\\\000\0\eng\d
001480932 019__ $$a1396975603$$a1397570616
001480932 020__ $$a9789819935376$$q(electronic bk.)
001480932 020__ $$a9819935377$$q(electronic bk.)
001480932 020__ $$z9789819935369
001480932 020__ $$z9819935369
001480932 0247_ $$a10.1007/978-981-99-3537-6$$2doi
001480932 035__ $$aSP(OCoLC)1398228555
001480932 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX
001480932 049__ $$aISEA
001480932 050_4 $$aTA169.6
001480932 08204 $$a620/.00452$$223/eng/20230919
001480932 1001_ $$aLi, Weihua,$$eauthor.
001480932 24510 $$aIntelligent fault diagnosis and health assessment for complex electro-mechanical systems /$$cWeihua Li, Xiaoli Zhang, Ruqiang Yan.
001480932 264_1 $$aSingapore :$$bSpringer,$$c2023.
001480932 300__ $$a1 online resource (xi, 467 pages) :$$billustrations (some color)
001480932 336__ $$atext$$btxt$$2rdacontent
001480932 337__ $$acomputer$$bc$$2rdamedia
001480932 338__ $$aonline resource$$bcr$$2rdacarrier
001480932 504__ $$aIncludes bibliographical references.
001480932 5050_ $$aChapter 1 Introduction -- Chapter 2 Supervised SVM based intelligent fault diagnosis methods -- Chapter 3 Semi-supervised Learning Based Intelligent Fault Diagnosis Methods -- Chapter 4 Manifold learning based intelligent fault diagnosis and prognostics -- Chapter 5 Deep learning based machinery fault diagnosis -- Chapter 6 Phase space reconstruction based on machinery system degradation tracking and fault prognostics -- Chapter 7 Complex electro-mechanical system operational reliability assessment and health maintenance.
001480932 506__ $$aAccess limited to authorized users.
001480932 520__ $$aBased on AI and machine learning, this book systematically presents the theories and methods for complex electro-mechanical system fault prognosis, intelligent diagnosis, and health state assessment in modern industry. The book emphasizes feature extraction, incipient fault prediction, fault classification, and degradation assessment, which are based on supervised-, semi-supervised-, manifold-, and deep learning; machinery degradation state tracking and prognosis by phase space reconstruction; and complex electro-mechanical system reliability assessment and health maintenance based on running state info. These theories and methods are integrated with practical industrial applications, which can help the readers get into the field more smoothly and provide an important reference for their study, research, and engineering practice.
001480932 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 19, 2023).
001480932 650_0 $$aFault location (Engineering)
001480932 650_0 $$aArtificial intelligence$$xIndustrial applications.$$xMedical applications$$0(DLC)sh 88003000
001480932 655_0 $$aElectronic books.
001480932 7001_ $$aZhang, Xiaoli,$$eauthor.
001480932 7001_ $$aYan, Ruqiang,$$eauthor.
001480932 77608 $$iPrint version:$$aLi, Weihua$$tIntelligent Fault Diagnosis and Health Assessment for Complex Electro-Mechanical Systems$$dSingapore : Springer Singapore Pte. Limited,c2023$$z9789819935369
001480932 852__ $$bebk
001480932 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-3537-6$$zOnline Access$$91397441.1
001480932 909CO $$ooai:library.usi.edu:1480932$$pGLOBAL_SET
001480932 980__ $$aBIB
001480932 980__ $$aEBOOK
001480932 982__ $$aEbook
001480932 983__ $$aOnline
001480932 994__ $$a92$$bISE