000			04577nam a22003615i 4500
001			287110
003			MX-SnUAN
005			20160429154530.0
007			cr nn 008mamaa
008			150903s2012 xxk| o |||| 0|eng d
020			_a9781447123804 _99781447123804
024	7		_a10.1007/9781447123804 _2doi
035			_avtls000339574
039		9	_a201509030317 _bVLOAD _c201404300402 _dVLOAD _y201402060938 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aTA213-215
100	1		_aMarwala, Tshilidzi. _eautor _9305386
245	1	0	_aCondition Monitoring Using Computational Intelligence Methods : _bApplications in Mechanical and Electrical Systems / _cby Tshilidzi Marwala.
264		1	_aLondon : _bSpringer London, _c2012.
300			_axv, 235 páginas 28 ilustraciones, 11 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aIntroduction to Condition Monitoring -- Data Gathering Methods -- Preprocessing and Feature Selection -- Condition Monitoring Using Neural Networks -- Condition Monitoring Using Support Vector Machines -- Condition Monitoring Using Neuro-fuzzy Methods -- Condition Monitoring Using Neuro-rough Methods -- Condition Monitoring Using Hidden Markov Models and Gaussian Mixture Models -- Condition Monitoring Using Hybrid Techniques -- Condition Monitoring Using Incremental Learning with Genetic Algorithms -- Conclusion.
520			_aCondition monitoring uses the observed operating characteristics of a machine or structure to diagnose trends in the signal being monitored and to predict the need for maintenance before a breakdown occurs. This reduces the risk, inherent in a fixed maintenance schedule, of performing maintenance needlessly early or of having a machine fail before maintenance is due either of which can be expensive with the latter also posing a risk of serious accident especially in systems like aeroengines in which a catastrophic failure would put lives at risk. The technique also measures responses from the whole of the system under observation so it can detect the effects of faults which might be hidden deep within a system, hidden from traditional methods of inspection. Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as: ·        fuzzy systems; ·        rough and neuro-rough sets; ·        neural and Bayesian networks; ·        hidden Markov and Gaussian mixture models; and ·        support vector machines. On-line learning methods such as Learn++ and ILUGA (incremental learning using genetic algorithms) are used to enable the classifiers to take on additional information and adjust to new condition classes by evolution rather than by complete retraining. Both the chosen methods have good incremental learning abilities with ILUGA, in particular, not suffering from catastrophic forgetting. Researchers studying computational intelligence and its applications will find Condition Monitoring Using Computational Intelligence Methods to be an excellent source of examples. Graduate students studying condition monitoring and diagnosis will find this alternative approach to the problem of interest and practitioners involved in fault diagnosis will be able to use these methods for the benefit of their machines and of their companies.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781447123798
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-2380-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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