Browse > Article
http://dx.doi.org/10.5391/JKIIS.2004.14.7.830

A Fault Diagnosis Based on Multilayer/ART2 Neural Networks  

Lee, In-Soo (상주대학교 전자전기공학부)
Yu, Du-Hyoung (상산전자공업고등학교)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.14, no.7, 2004 , pp. 830-837 More about this Journal
Abstract
Neural networks-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the multilayer neural network-based nominal model output cross a Predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by nultilayer/ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.
Keywords
Fault detection; fault isolation; multilayer NN; ART2 NN; nonlinear system;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 V. Venkatasubramanian, R. Vaidyanathan and Y. Yamamoto, 'Process fault detection and diagnosis using neural networks-steady state processes,' Computers Chem. Engng., vol. 14, no. 7, pp. 699-712, 1990   DOI   ScienceOn
2 이인수, '신경회로망기반 다중고장모델에 의한 비선형시스템의 고장감지와 분류', 대한전자공학회논문집, vol. 39, no. 1, pp. 42-50, 2002
3 D. E. Rumelhart and J. L. McClelland, Parallel distributed processing: explorations in the microstructure of cognition. vol. 1, MIT Press, Reading, MA, 1986
4 S. Y. Kung, Digital Neural Networks, Prentice Hall, 1993
5 J. D. Cryer, Time Series Analysis, Duxbury Press, 1986
6 J. Wagner and R. Shoureshi, 'A failure isolation strategy for therrnofluid system diagnostics,' ASME J. Eng. for Industry, vol. 115, pp. 459-465, 1993   DOI   ScienceOn
7 M. A. Kramer and J. A. Leonard, 'Diagnosis using backpropagation neural networks-analysis and criticism,' Computers Chem. Engng., vol. 14, no. 12, pp. 1323-1338, 1990   DOI   ScienceOn
8 T. Sorsa, H. N. Koivo and H. Koivisto, 'Neural networks in process fault diagnosis,' IEEE Trans. Syst., Man and Cybern., vol. 21, no. 4, pp. 815-825, 1991   DOI   ScienceOn
9 J. C. Hoskins and D. M. Himmelblau, 'Artificial neural network models of knowledge representation in chemical engineering,' Computers Chem. Engng., vol. 12, no. 9, pp. 881-890, 1988   DOI   ScienceOn
10 A. Srinivasan and C. Batur, 'Hopfield/ART-1 neural network-based fault detection and isolation,' IEEE Trans. Neural Networks, vol. 5, no. 6, pp. 890-899, 1994   DOI   ScienceOn
11 E. Eryurek and B. R. Upadhyaya, 'Sensor validation for power plants using adaptive back propagation neural network,' IEEE Trans. Nuclear Science, vol. 37, no. 2, pp. 1040-1047, 1990   DOI   ScienceOn
12 R. Isermann, 'Process fault detection based on modeling and estimation methods-a survey,' Automatica, vol. 20, no. 4, pp. 387-404, 1984   DOI   ScienceOn
13 M. M. Polycarpou and A. T. Vemuri, Learning methodology for failure detection and accommodation,' IEEE Contr. Syst. Mag., pp. 16-24, 1995