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http://dx.doi.org/10.5391/JKIIS.2004.14.1.001

Neural Network-Based Sensor Fault Diagnosis in the Gas Monitoring System  

Lee, In-Soo (상주대학교 전자전기공학부)
Cho, Jung-Hwan (경북대학교 전자ㆍ전기ㆍ컴퓨터공학부)
Shim, Chang-Hyun (㈜센스엔센서)
Lee, Duk-Dong (경북대학교 전자ㆍ전기ㆍ컴퓨터공학부)
Jeon, Gi-Joon (경북대학교 전자ㆍ전기ㆍ컴퓨터공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.14, no.1, 2004 , pp. 1-8 More about this Journal
Abstract
In this paper, we propose neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, ART2 neural network is used for fault isolation. The performance and effectiveness of the proposed ART2 neural network based fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.
Keywords
Gas monitoring system; sensor fault diagnosis; thermal modulation; ART2 neural network;
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  • Reference
1 W. Gopel, "Chemical imaging: Concepts and visions for electronic and bio-electronic noses," Sens. Actuators B, vol. 52, pp. 125-142, 1998.   DOI   ScienceOn
2 R. Patton, P. Frank and R. Clark, Fault Diagnosis in Dynamic Systems; Theory and Application, Prentice Hall, 1989.
3 M. M. Polycarpou and A. T. Vemuri, "Learning methodology for failure detection and accommodation," IEEE Contr. Syst. Mag., pp.16-24, 1995.
4 A. P. Lee and B. J. Reedy, "Temperature modulation in semiconductor gas sensing," Sens. and Actuators B, vol. 60, pp. 35-42, 1999.   DOI   ScienceOn
5 E. Y. Chow and A. S. Willsky, "Analytical redundancy and the design of robust failure detection systems," IEEE Trans. Automat. Contr., vol. AC-29, no. 7, pp. 603-614, 1984.
6 이인수,신필재,전기준, "ART2 신경회로망을 이용한 선형 시스템의 다중고장진단", 제어.자동화.시스템공학회 논문지, 제 3권, 제 3호, pp. 244-251, 1997.   과학기술학회마을
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 C. Delpha, M. Siadat, and M. Lumbreras, "Identification of Forane R134a in an air conditioned atmosphere with a TGS sensor array," IEEE Trans. on Instrumentation and Measurement, vol. 50, pp. 1370 -1374, 2001.   DOI   ScienceOn
9 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
10 C. H. Dagli, Artificial Neural Networks for Intelligent Manufacturing, Chapman and Hall, 1994.
11 T. Sorsa and H. K. Koivo "Application of artificial neural networks in process fault diagnosis," Automatica, vol. 29, no. 4, pp. 843-849, 1993.   DOI   ScienceOn
12 L. I. Burke, "Competitive learning based approaches to tool-wear identification," IEEE Trans. Syst., Man and Cybern., vol. 22, no. 3, pp. 559-563, 1991.   DOI   ScienceOn
13 M. Pardo, G. Faglia, G. Sberveglieri, M. Corte, F. Masulli and M. Riani, "Monitoring reliability of sensors in an array by neural networks", Sens. Actuators B, vol. 67, pp. 128-133, 2000.   DOI   ScienceOn
14 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
15 M. A. Massoumnia, "A geometric approach to the synthesis of failure detection filters," IEEE Trans. Automat. Contr., vol. AC 31, no. 9, pp. 839-846, 1986.
16 S. Y. Kung, Digital Neural Networks, Prentice Hall, 1993.