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001 978-3-540-79872-9
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005 20161121230550.0
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008 100301s2008 gw | s |||| 0|eng d
020 _a9783540798729
_9978-3-540-79872-9
024 7 _a10.1007/978-3-540-79872-9
_2doi
050 4 _aTJ210.2-211.495
050 4 _aTJ163.12
072 7 _aTJFM
_2bicssc
072 7 _aTJFD
_2bicssc
072 7 _aTEC004000
_2bisacsh
072 7 _aTEC037000
_2bisacsh
082 0 4 _a629.8
_223
100 1 _aPatan, Krzysztof.
_eauthor.
245 1 0 _aArtificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes
_h[electronic resource] /
_cby Krzysztof Patan.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2008.
300 _aXXII, 206 p. 93 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Control and Information Sciences,
_x0170-8643 ;
_v377
505 0 _aModelling Issue in Fault Diagnosis -- Locally Recurrent Neural Networks -- Approximation Abilities of Locally Recurrent Networks -- Stability and Stabilization of Locally Recurrent Networks -- Optimum Experimental Design for Locally Recurrent Networks -- Decision Making in Fault Detection -- Industrial Applications -- Concluding Remarks and Further Research Directions.
520 _aAn unappealing characteristic of all real-world systems is the fact that they are vulnerable to faults, malfunctions and, more generally, unexpected modes of - haviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and e?ective fault diagnosis strategies. This is especially true for engineering systems,whose complexity is permanently growing due to the inevitable development of modern industry as well as the information and communication technology revolution. Indeed, the design and operation of engineering systems require an increased attention with respect to availability, reliability, safety and fault tolerance. Thus, it is natural that fault diagnosis plays a fundamental role in modern control theory and practice. This is re?ected in plenty of papers on fault diagnosis in many control-oriented c- ferencesand journals.Indeed, a largeamount of knowledgeon model basedfault diagnosis has been accumulated through scienti?c literature since the beginning of the 1970s. As a result, a wide spectrum of fault diagnosis techniques have been developed. A major category of fault diagnosis techniques is the model based one, where an analytical model of the plant to be monitored is assumed to be available.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aSystem theory.
650 0 _aStatistical physics.
650 0 _aDynamical systems.
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aMechatronics.
650 1 4 _aEngineering.
650 2 4 _aControl, Robotics, Mechatronics.
650 2 4 _aSystems Theory, Control.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aStatistical Physics, Dynamical Systems and Complexity.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540798712
830 0 _aLecture Notes in Control and Information Sciences,
_x0170-8643 ;
_v377
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-540-79872-9
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c500644
_d500644