LEADER 05050nam 22007095 450 001 9910148854303321 005 20250625083632.0 024 7 $a10.1007/978-3-319-44742-1 035 $a(CKB)3710000000918157 035 $a(DE-He213)978-3-319-44742-1 035 $a(MiAaPQ)EBC4723678 035 $a(PPN)196326095 035 $a(EXLCZ)993710000000918157 100 $a20161024d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPrognostics and Health Management of Engineering Systems $eAn Introduction /$fby Nam-Ho Kim, Dawn An, Joo-Ho Choi 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIV, 347 p. 166 illus., 155 illus. in color.) 311 08$a3-319-44740-8 311 08$a3-319-44742-4 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aIntroduction -- Tutorials for Prognostics -- Bayesian Statistics for Prognostics -- Physics-Based Prognostics -- Data-Driven Prognostics -- Study on Attributes of Prognostic Methods -- Applications of Prognostics. 330 $aThis book introduces the methods for predicting the future behavior of a system?s health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application. Among the many topics discussed in-depth are: ? Prognostics tutorials using least-squares ? Bayesian inference and parameter estimation ? Physics-based prognostics algorithms including nonlinear least squares, Bayesian method, and particle filter ? Data-driven prognostics algorithms including Gaussian process regression and neural network ? Comparison of different prognostics algorithms The authors also present several applications of prognostics in practical engineering systems, including wear in a revolute joint, fatigue crack growth in a panel, prognostics using accelerated life test data, fatigue damage in bearings, and more. Prognostics tutorials with a Matlab code using simple examples are provided, along with a companion website that presents Matlab programs for different algorithms as well as measurement data. Each chapter contains a comprehensive set of exercise problems, some of which require Matlab programs, making this an ideal book for graduate students in mechanical, civil, aerospace, electrical, and industrial engineering and engineering mechanics, as well as researchers and maintenance engineers in the above fields. 606 $aRenewable energy resources 606 $aAerospace engineering 606 $aAstronautics 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aBuilding materials 606 $aCivil engineering 606 $aRenewable and Green Energy$3https://scigraph.springernature.com/ontologies/product-market-codes/111000 606 $aAerospace Technology and Astronautics$3https://scigraph.springernature.com/ontologies/product-market-codes/T17050 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aStructural Materials$3https://scigraph.springernature.com/ontologies/product-market-codes/Z11000 606 $aCivil Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T23004 615 0$aRenewable energy resources. 615 0$aAerospace engineering. 615 0$aAstronautics. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aBuilding materials. 615 0$aCivil engineering. 615 14$aRenewable and Green Energy. 615 24$aAerospace Technology and Astronautics. 615 24$aSignal, Image and Speech Processing. 615 24$aStructural Materials. 615 24$aCivil Engineering. 676 $a621.042 700 $aKim$b Nam H.$4aut$4http://id.loc.gov/vocabulary/relators/aut$0771504 702 $aAn$b Dawn$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aChoi$b Joo-Ho$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910148854303321 996 $aPrognostics and Health Management of Engineering Systems$94398239 997 $aUNINA