000 04458nam a22006015i 4500
001 978-3-319-44742-1
003 DE-He213
005 20220801222419.0
007 cr nn 008mamaa
008 161024s2017 sz | s |||| 0|eng d
020 _a9783319447421
_9978-3-319-44742-1
024 7 _a10.1007/978-3-319-44742-1
_2doi
050 4 _aTJ807-830
072 7 _aTHX
_2bicssc
072 7 _aTEC031010
_2bisacsh
072 7 _aTHV
_2thema
082 0 4 _a621.042
_223
100 1 _aKim, Nam-Ho.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_961355
245 1 0 _aPrognostics and Health Management of Engineering Systems
_h[electronic resource] :
_bAn Introduction /
_cby Nam-Ho Kim, Dawn An, Joo-Ho Choi.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXIV, 347 p. 166 illus., 155 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Tutorials for Prognostics -- Bayesian Statistics for Prognostics -- Physics-Based Prognostics -- Data-Driven Prognostics -- Study on Attributes of Prognostic Methods -- Applications of Prognostics.
520 _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.
650 0 _aRenewable energy sources.
_94906
650 0 _aAerospace engineering.
_96033
650 0 _aAstronautics.
_961356
650 0 _aSignal processing.
_94052
650 0 _aBuilding materials.
_931878
650 0 _aCivil engineering.
_910082
650 1 4 _aRenewable Energy.
_913722
650 2 4 _aAerospace Technology and Astronautics.
_961357
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aStructural Materials.
_931883
650 2 4 _aCivil Engineering.
_910082
700 1 _aAn, Dawn.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_961358
700 1 _aChoi, Joo-Ho.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_961359
710 2 _aSpringerLink (Online service)
_961360
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319447407
776 0 8 _iPrinted edition:
_z9783319447414
776 0 8 _iPrinted edition:
_z9783319831268
856 4 0 _uhttps://doi.org/10.1007/978-3-319-44742-1
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80741
_d80741