Sensor Fault Identification Using Autoregressive Models and the Mutual Information Concept


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This paper presents a combined approach for sensor fault identification looking for changes within one channel on one hand and for changes between the different channels on the other hand. The first method is based on the identification of autoregressive (AR) models from the reference time signals for each sensor channel separately. The reference models are then used for the prediction of the future sensors signals. The statistical properties of the residuals between this prediction and the true measurement allow a statement about changes of the sensor signals. The second method is based on the concept of mutual information between two signals X and Y from two different sensors. Mutual information or transinformation measures the information about the channel X that is shared by Y. This requires a certain redundancy of information represented in the different sensor signals. It can be seen that the mutual information changes as soon as a sensor fault occurs because the sensor fault information is not present in the other sensor signals.



Edited by:

L. Garibaldi, C. Surace, K. Holford and W.M. Ostachowicz




P. Kraemer and C. P. Fritzen, "Sensor Fault Identification Using Autoregressive Models and the Mutual Information Concept", Key Engineering Materials, Vol. 347, pp. 387-392, 2007

Online since:

September 2007




[1] R. Dunia, S. J. Qin, T. F. Edgar and T. J. McAvoy in: AiChe Journal, Vol. 42, No. 10 (1996), pp.2797-2812.

[2] J. Kullaa in: Proc. ISMA2006 (on CD-ROM), Leuven (Sept. 18-20, 2006).

[3] G. Kerschen, P. De Boe, J. -C. Golinval and K. Worden in: Proc. 2nd Europ. Workshop on SHM 2004, (July 7-9, 2004), pp.819-827.

[4] D. L. Mattern, L. C. Jaw, T. -H. Guo, R. Graham and W. McCoy in: 34 th Joint Propulsion Conference, Cleveland, Ohio, USA (July 12-15, 1998).

[5] K. Worden in: Proc. IMAC XXI, Orlando, USA (2003).

[6] H. Sohn and C. R. Farrar in: Smart Materials and Structures, Vol. 10 (2001), pp.446-451.

[7] H. Sohn, C. R. Farrar, N. Hunter and K. Worden in: Transaction of the ASME, Vol. 123 (2001), pp.706-711.

[8] A. Kraskov, H. Stögbauer and P. Grassberger in: Physical Review E, 69 (2004), 066138I03.

[9] I. Trendafilova and W. Heylen in: Europ. COST F3 Conference on System Identification & SHM, Madrid, Vol. 1 (2000), pp.147-156.

[10] G. E. P. Box, G. M. Jenkins and G. C. Reinsel: Time Series Analysis, Forecasting and Control, 3. Edition, Prentice Hall, New Jersey (1994).

[11] M. B. Priestley: Spectral analysis and time series, Probability and mathematical statistics, Academic Press, Harcourt Brace Jovanovich Publishers (1992).