Application of Computational Intelligence for Sensor Fault Detection and Isolation
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:51375", author = "A. Jabbari and R. Jedermann and W. Lang", title = "Application of Computational Intelligence for Sensor Fault Detection and Isolation", 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.", volume = "1", number = "9", pages = "2657-6", }
