000			04813nam a22004935i 4500
001			978-1-4471-2380-4
003			DE-He213
005			20140220083235.0
007			cr nn 008mamaa
008			120124s2012 xxk| s |||| 0|eng d
020			_a9781447123804 _9978-1-4471-2380-4
024	7		_a10.1007/978-1-4471-2380-4 _2doi
050		4	_aTA213-215
072		7	_aTGBN _2bicssc
072		7	_aTEC046000 _2bisacsh
082	0	4	_a621.8 _223
100	1		_aMarwala, Tshilidzi. _eauthor.
245	1	0	_aCondition Monitoring Using Computational Intelligence Methods _h[electronic resource] : _bApplications in Mechanical and Electrical Systems / _cby Tshilidzi Marwala.
264		1	_aLondon : _bSpringer London, _c2012.
300			_aXV, 235p. 28 illus., 11 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 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.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aSystem safety.
650		0	_aStructural control (Engineering).
650	1	4	_aEngineering.
650	2	4	_aMachinery and Machine Elements.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aQuality Control, Reliability, Safety and Risk.
650	2	4	_aOperating Procedures, Materials Treatment.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781447123798
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4471-2380-4
912			_aZDB-2-ENG
999			_c100667 _d100667