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006			m o d
007			cr cnu---unuuu
008			161027s2017 sz ob 001 0 eng d
020			_a3319447424 _q(electronic bk.)
020			_a9783319447421 _q(electronic bk.)
020			_z3319447408
020			_z9783319447407
035			_a(OCoLC)961271894 _z(OCoLC)961412146 _z(OCoLC)961815065 _z(OCoLC)964924265 _z(OCoLC)974649501 _z(OCoLC)1005772436
040			_aN$T _cN$T _dYDX _dN$T _dAZU _dIDEBK _dGW5XE _dCOO _dOCLCF _dCNCGM _dUAB _dUPM _dIOG _dESU _dZ5A _dJBG _dIAD _dICN _dOTZ _dOCLCQ _dVT2 _dU3W _dES-MaUEC _bspa
050		4	_aTA168 _b.K566 2017 EB
100	1		_aKim, Nam-ho, _eautor
245	1	0	_aPrognostics and health management of engineering systems : _ban introduction _cNam-Ho Kim, Dawn An, Joo-Ho Choi.
264		1	_aCham, Switzerland _bSpringer _c[2017]
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas e índice
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	3		_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		7	_aIngeniería de sistemas _xTesting. _2embne _0(OCoLC)fst01903121 _0 _9138451
700	1		_aAn, Dawn, _eautor
700	1		_aChoi, Joo-Ho, _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-44742-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017A
998			_b02/2018 _dz _e- _zSI
999			_c94778 _d94778 _x1