DocumentCode :
2752302
Title :
A Method of Fast Fault Detection Based on ARMA and Neural Network
Author :
Yang, Tianqi
Author_Institution :
Dept. of Comput. Sci., Jinan Univ., Guangzhou
Volume :
2
fYear :
0
fDate :
0-0 0
Firstpage :
5438
Lastpage :
5441
Abstract :
Current fault detection systems lack the ability to generalize from previously observed patterns to detect even slight variations of unknown faults. In this paper, ARMA model combining with a Hopfield-model net is proposed for describing a approach that provides the ability to generalize from previously observed behavior to recognize future behavior. The approach can be used for fault detection in order to analyze and detect novel anomaly patterns. Meanwhile, a feedback neural network was used to predict the `expected values´ of the anomaly; using the neural network is especially better since it can improve the detection rate without increasing the false positives. Experiments show events and variance of anomaly patterns
Keywords :
Hopfield neural nets; autoregressive moving average processes; fault diagnosis; ARMA; Hopfield-model net; anomaly patterns detection; fast fault detection; fault detection systems; feedback neural network; Automation; Computer science; Electronic mail; Fault detection; Intelligent control; Kalman filters; Neural networks; Neurofeedback; Pattern analysis; ARMA; fast fault detection; neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
Type :
conf
DOI :
10.1109/WCICA.2006.1714111
Filename :
1714111
Link To Document :
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