Fault diagnosis in complex systems using artificial neural networks

Tzafestas, SG; Dalianis, PJ

dc.contributor.author	Tzafestas, SG	en
dc.contributor.author	Dalianis, PJ	en
dc.date.accessioned	2014-03-01T02:41:01Z
dc.date.available	2014-03-01T02:41:01Z
dc.date.issued	1994	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30326
dc.subject	Adaptive Method	en
dc.subject	Artificial Neural Network	en
dc.subject	backpropagation	en
dc.subject	Complex System	en
dc.subject	Condition Monitoring	en
dc.subject	Fault Diagnosis	en
dc.subject	Manufacturing System	en
dc.subject	Nonlinear System	en
dc.subject	Radial Basis Function	en
dc.subject	Real World Application	en
dc.subject	System Performance	en
dc.subject	Neural Network	en
dc.subject.other	Adaptive systems	en
dc.subject.other	Algorithms	en
dc.subject.other	Backpropagation	en
dc.subject.other	Computer simulation	en
dc.subject.other	Error detection	en
dc.subject.other	Function evaluation	en
dc.subject.other	Knowledge based systems	en
dc.subject.other	Large scale systems	en
dc.subject.other	Mathematical models	en
dc.subject.other	Nonlinear control systems	en
dc.subject.other	Online systems	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Adaptive methods	en
dc.subject.other	Backpropagation networks	en
dc.subject.other	Complex nonlinear functions	en
dc.subject.other	Expert diagnostic knowledge	en
dc.subject.other	Fault diagnosis	en
dc.subject.other	Manufacturing systems	en
dc.subject.other	Neuron	en
dc.subject.other	Radial basis function	en
dc.subject.other	Symptom interpretation	en
dc.subject.other	Neural networks	en
dc.title	Fault diagnosis in complex systems using artificial neural networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/CCA.1994.381206	en
heal.identifier.secondary	http://dx.doi.org/10.1109/CCA.1994.381206	en
heal.publicationDate	1994	en
heal.abstract	Very complex technical and other physical processes require sophisticated methods of fault diagnosis and on-line condition monitoring. Various conventional techniques have already been well investigated and presented in the literature. However, the last few years, a lot of attention is given to adaptive methods based on artificial neural networks, which can significantly improve the symptom interpretation and system performance in case of malfunctioning. Such methods are especially considered in cases where no explicit algorithms or models for the problem under investigation exist. In such problems, automatic interpretation of faulty symptoms with the use of artificial neural network classifiers is recommended. Two different models of artificial neural networks, the extended backpropagation and the radial basis function, are discussed and applied with appropriate simulations for a real world applications in a chemical manufacturing plant.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	Proceedings of the IEEE Conference on Control Applications	en
dc.identifier.doi	10.1109/CCA.1994.381206	en
dc.identifier.volume	2	en
dc.identifier.spage	877	en
dc.identifier.epage	882	en