Title :
Intelligent fault detection and diagnostics system on rule-based neural network approach
Author :
Arseniev, Dmitry G. ; Lyubimov, Boris E. ; Shkodyrev, Viacheslav P.
Author_Institution :
St. Petersburg State Polytech. Univ., St. Petersburg, Russia
Abstract :
Modern industrial systems can´t exist without fault detection and diagnostics subsystem. Creation of such subsystem becomes a challenging task. Often it´s more difficult than creation of the rest system´s parts. This paper provides an approach for building fault detection and diagnostics system based on artificial neural networks, automatic training method for such systems and investigates different aspects of this method.
Keywords :
fault diagnosis; learning (artificial intelligence); neural nets; production engineering computing; artificial neural networks; automatic training method; diagnostics system; industrial systems; intelligent fault detection; rule-based neural network approach; Artificial neural networks; Degradation; Engines; Fault detection; Intelligent networks; Intelligent systems; Knowledge based systems; Neural networks; Prototypes; Spreadsheet programs;
Conference_Titel :
Control Applications, (CCA) & Intelligent Control, (ISIC), 2009 IEEE
Conference_Location :
Saint Petersburg
Print_ISBN :
978-1-4244-4601-8
Electronic_ISBN :
978-1-4244-4602-5
DOI :
10.1109/CCA.2009.5281003
