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001453133 001__ 1453133
001453133 003__ OCoLC
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001453133 019__ $$a1348480980
001453133 020__ $$a9789811691317$$q(electronic bk.)
001453133 020__ $$a9811691312$$q(electronic bk.)
001453133 020__ $$z9789811691300
001453133 020__ $$z9811691304
001453133 0247_ $$a10.1007/978-981-16-9131-7$$2doi
001453133 035__ $$aSP(OCoLC)1348636572
001453133 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF$$dOCLCQ
001453133 049__ $$aISEA
001453133 050_4 $$aTA169.6
001453133 08204 $$a620/.004$$223/eng/20221031
001453133 1001_ $$aLei, Yaguo,$$eauthor.
001453133 24510 $$aBig-data driven intelligent fault diagnosis and prognosis for mechanical systems /$$cYaguo Lei, Naipeng Li, Xiang Li.
001453133 264_1 $$aSingapore :$$bSpringer ;$$a[Xi'an] :$$bXi'an Jiaotong University Press,$$c[2023]
001453133 264_4 $$c©2023
001453133 300__ $$a1 online resource (xiii, 281 pages) :$$billustrations (chiefly color)
001453133 336__ $$atext$$btxt$$2rdacontent
001453133 337__ $$acomputer$$bc$$2rdamedia
001453133 338__ $$aonline resource$$bcr$$2rdacarrier
001453133 504__ $$aIncludes bibliographical references.
001453133 5050_ $$aIntro -- Preface -- Contents -- About the Authors -- 1 Introduction and Background -- 1.1 Introduction -- 1.1.1 AI Technologies for Data Processing -- 1.1.2 Big Data-Driven Intelligent Predictive Maintenance -- 1.1.3 Big Data Analytics Platform Practices -- 1.2 Overview of Big Data-Driven PHM -- 1.2.1 Data Acquisition -- 1.2.2 Data Processing -- 1.2.3 Diagnosis -- 1.2.4 Prognosis -- 1.2.5 Maintenance -- 1.3 Preface to Book Chapters -- References -- 2 Conventional Intelligent Fault Diagnosis -- 2.1 Introduction -- 2.2 Typical Neural Network-Based Methods
001453133 5058_ $$a2.2.1 Introduction to Neural Networks -- 2.2.2 Intelligent Diagnosis Using Radial Basis Function Network -- 2.2.3 Intelligent Diagnosis Using Wavelet Neural Network -- 2.2.4 Epilog -- 2.3 Statistical Learning-Based Methods -- 2.3.1 Introduction to Statistical Learning -- 2.3.2 Intelligent Diagnosis Using Support Vector Machine -- 2.3.3 Intelligent Diagnosis Using Relevant Vector Machine -- 2.3.4 Epilog -- 2.4 Conclusions -- References -- 3 Hybrid Intelligent Fault Diagnosis -- 3.1 Introduction -- 3.2 Multiple WKNN Fault Diagnosis -- 3.2.1 Motivation
001453133 5058_ $$a3.2.2 Diagnosis Model Based on Combination of Multiple WKNN -- 3.2.3 Intelligent Diagnosis Case Study of Rolling Element Bearings -- 3.2.4 Epilog -- 3.3 Multiple ANFIS Hybrid Intelligent Fault Diagnosis -- 3.3.1 Motivation -- 3.3.2 Multiple ANFIS Combination with GA -- 3.3.3 Fault Diagnosis Method Based on Multiple ANFIS Combination -- 3.3.4 Intelligent Diagnosis Case of Rolling Element Bearings -- 3.3.5 Epilog -- 3.4 A Multidimensional Hybrid Intelligent Method -- 3.4.1 Motivation -- 3.4.2 Multiple Classifier Combination -- 3.4.3 Diagnosis Method Based on Multiple Classifier Combination
001453133 5058_ $$a3.4.4 Intelligent Diagnosis Case of Gearboxes -- 3.4.5 Epilog -- 3.5 Conclusions -- References -- 4 Deep Transfer Learning-Based Intelligent Fault Diagnosis -- 4.1 Introduction -- 4.2 Deep Belief Network for Few-Shot Fault Diagnosis -- 4.2.1 Motivation -- 4.2.2 Deep Belief Network-Based Diagnosis Model with Continual Learning -- 4.2.3 Few-Shot Fault Diagnosis Case of Industrial Robots -- 4.2.4 Epilog -- 4.3 Multi-Layer Adaptation Network for Fault Diagnosis with Unlabeled Data -- 4.3.1 Motivation -- 4.3.2 Multi-Layer Adaptation Network-Based Diagnosis Model
001453133 5058_ $$a4.3.3 Fault Diagnosis Case of Locomotive Bearings with Unlabeled Data -- 4.3.4 Epilog -- 4.4 Deep Partial Adaptation Network for Domain-Asymmetric Fault Diagnosis -- 4.4.1 Motivation -- 4.4.2 Deep Partial Transfer Learning Net-Based Diagnosis Model -- 4.4.3 Partial Transfer Diagnosis of Gearboxes with Domain Asymmetry -- 4.4.4 Epilog -- 4.5 Instance-Level Weighted Adversarial Learning for Open-Set Fault Diagnosis -- 4.5.1 Motivation -- 4.5.2 Instance-Level Weighted Adversarial Learning-Based Diagnosis Model -- 4.5.3 Fault Diagnosis Case of Rolling Bearing Datasets -- 4.5.4 Epilog
001453133 506__ $$aAccess limited to authorized users.
001453133 520__ $$aThis book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies.
001453133 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 31, 2022).
001453133 650_0 $$aFault location (Engineering)
001453133 650_0 $$aMechanical engineering$$xData processing.
001453133 650_0 $$aBig data.
001453133 655_0 $$aElectronic books.
001453133 7001_ $$aLi, Naipeng,$$eauthor.
001453133 7001_ $$aLi, Xiang,$$eauthor.
001453133 77608 $$iPrint version: $$z9811691304$$z9789811691300$$w(OCoLC)1287127323
001453133 852__ $$bebk
001453133 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-9131-7$$zOnline Access$$91397441.1
001453133 909CO $$ooai:library.usi.edu:1453133$$pGLOBAL_SET
001453133 980__ $$aBIB
001453133 980__ $$aEBOOK
001453133 982__ $$aEbook
001453133 983__ $$aOnline
001453133 994__ $$a92$$bISE