Application of Computational Intelligence for Sensor Fault Detection and Isolation

Authors: A. Jabbari, R. Jedermann, W. Lang

Abstract:

The new idea of this research is application of a new fault detection and isolation (FDI) technique for supervision of sensor networks in transportation system. In measurement systems, it is necessary to detect all types of faults and failures, based on predefined algorithm. Last improvements in artificial neural network studies (ANN) led to using them for some FDI purposes. In this paper, application of new probabilistic neural network features for data approximation and data classification are considered for plausibility check in temperature measurement. For this purpose, two-phase FDI mechanism was considered for residual generation and evaluation.

Keywords: Fault detection and Isolation, Neural network, Temperature measurement, measurement approximation and classification.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056727

References:

[1] A. Jain, "Frost prediction using artificial neural networks: A temperature prediction approach," M.Sc. thesis, Artificial Intelligence Center, University of Georgia, Athens, GA, 2003.
[2] B. Kosko, "Neural Networks and Fuzzy Systems", Englewood Cliffs, NJ: Prentice-Hall, 1992.
[3] D. Hall, "Mathematical Techniques in Multisensor Data Fusion", Boston, MA: Artech House, 1992.
[4] D. L. Hall and J. Llinas, "An introduction to multisensor data fusion," Proc. IEEE, vol. 85, pp. 6-23, Jan. 1997.
[5] D. F. Specht and H. Romsdahl, "Experience with Adaptive Probabilistic Neural Networks and Adaptive General Regression Neural Networks", Proceedings of the IEEE World Congress on Computational Intelligence, 2, 1203-1208, 1994.
[6] D. F. Specht and P. D. Shapiro, "Generalization accuracy of Probabilistic Neural Networks compared with back-propagation networks.", In Proceedings of the International Joint Conference on Neural Networks, pp. 887-892, 1991.
[7] E. Vonk, L. C. Jain, and R. P., Johnson, "Automatic Generation of Neural Network Architecture using Evolutionary Computing", World Scientific Publishing Company, Singapore, 1997.
[8] F. Yalcinnkaya, D. P Atherton, H. Calis, and E. T. Powner, "Intelligent sensors: The way forward to decentralized intelligence and control," in Proc. Int. Conf. Control -98 UKACC, vol. 1, pp. 358-363, 1998.
[9] G. Betta, and Pietrosanto, A., ÔÇÿÔÇÿInstrument Fault Detection and Isolation: State of the Art and New Research Trends,-- IEEE Transactions of Instrumentation and Measurement, 2000.
[10] G. Betta, M. Dell-Isola, C. Liguori, and A. Pietrosanto, "Expert systems for the detection and isolation of faults on low-accuracy sensor systems," in Proc. IEEE Workshop ET&VS-IM/97, Niagara Falls, Ont., Canada, pp. 39-48, May 1997.
[11] G. Betta, M. Dell-Isola, C. Liguori, and A. Pietrosanto, "An artificial intelligence-based instrument fault detection and isolation scheme for air conditioning systems," in Proc. XIV IMEKO World Congr., vol. VII, Tampere, Finland, pp. 88-95, June 1997.
[12] H. Kirsch and K. Kroschel, "Applying Bayesian networks to fault diagnosis," in Proc. Third IEEE Conf. Control Applications, Glasgow, U.K., Aug. 1994, pp. 895-900.
[13] L. A. Klein, "Sensor and Data Fusion Concepts and Applications," in SPIE Optical Engineering Press. Bellingham, WA: Tutorial Texts, vol. 14, p. 131, 1993.
[14] N. Cristianini and J. Shawe-Taylor, "An introduction to support vector machines and other kernel-based learning methods", Cambridge University Press, 2000.
[15] O. Cohen and Y. Edan, "Adaptive sensor fusion framework for autonomous vehicles," in Proc. IASTED Int. Conf., Artificial Intelligence Applications, Marbella, Spain, 2001, pp. 282-286.
[16] P. Balle, D. Fussel, and O. Hecker, "Detection and isolation of sensor faults on nonlinear processes based on local linear models," in Proc. Amer. Control Conf., Albuquerque, NM, June 1997, pp. 468-472.
[17] P. D. Wasserman, "Advanced Methods in Neural Computing", New York, Van Nostrand Reinhold, 1993, pp. 35-55.
[18] P. K. Varshney, "Multisensor data fusion," Electron. Comm. Eng. J., vol. 9, no. 6, pp. 245-253, 1997.
[19] P. G. O-Reilly, "Local sensor fault detection using Bayesian decision theory," in UKACC Int. Conf. Control -98, Sept. 1-4, 1998, pp. 247- 251.
[20] P. H. Ibarguengoytia, L. E. Sucar, and S. Vadera, ÔÇÿÔÇÿA Probabilistic Model for Sensor Validation,-- Proc. Conf. on Uncertainty in Artificial Intelligence, Portland, OR, 1996.
[21] R. P. Leger, Wm. J. Garland and W. F. S. Poehlman, "Fault detection and diagnosis using statistical control charts and artificial neural networks", Artificial Intelligence in Engineering 12, 1998, pp. 35-41.
[22] R. Isermann and P. Ball, "Trends in the application of model-based fault detection and diagnosis of technical processes", Control Eng. Practice, Vol. 5, No. 5, 1997, pp. 709-719.
[23] R. C. Luo, Y. Chin-Chen, and L. S. Kuo, "Multi sensor fusion and integration: approaches, applications and future research directions," IEEE Sensors J., vol. 2, no. 2, pp. 107-119, Apr. 2002.
[24] S. Chen, C.F.N. Cowan, and P.M. Grant, "Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks," IEEE Transactions on Neural Networks, Vol. 2, No. 2, March 1991, pp. 302- 309.
[25] T. M. Chen and R. C. Luo, "Multilevel multi agent based team decision fusion for autonomous tracking system," Mach. Intell. Robot. Control, vol. 1, no. 2, pp. 63-69, 1999.
[26] Y. M. Chen and M. L. Lee, "Neural networks-based scheme for system failure detection and diagnosis", Mathematics and Computers in Simulation 58, 2002, 101-109.
[27] W.B. Lyons, C. Flanagan, E. Lewis, H. Ewald, and S. Lochmann, "Interrogation of multipoint optical fiber sensor signals based on artificial neural network pattern recognition techniques", Sensors and Actuators A 114, 2004, pp. 7-12.