Fault detection and isolation using concatenated wavelet transform variances and discriminant analysis

IEEE Transactions on Signal Processing

Volumn 54, Issue 5, 2006, Pages 1727-1736

  Gonzalez, G D ^a   Paut, R ^a   Cipriano, A ^b   Miranda, D R ^a   Ceballos, G E ^a  

^a UNIVERSITY OF CHILE   (Chile)

^b PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE   (Chile)

Author keywords

Discriminant analysis; Fault detection; Fault isolation; Wavelet transform

Indexed keywords

ESTIMATION; MATHEMATICAL MODELS; MATRIX ALGEBRA; PRINCIPAL COMPONENT ANALYSIS; RANDOM PROCESSES;

DISCRIMINANT ANALYSIS; FAULT DETECTION; FAULT ISOLATION;

WAVELET TRANSFORMS;

EID: 33646036335     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2006.872608     Document Type: Article

References (15)

1. J. Chen and R. J. Patton, Robust Model-Based Fault Diagnosis for Dynamic Systems. Norwood, MA: Kluwer Academic, 1999, pp. 2-3.
2. R. Isermann, "Process fault detection based on modeling and estimation methods - A survey," Automatica, vol. 20, pp. 387-404, Jul. 1984.
3. R. Isermann, "Trends in the application of model based fault detection and diagnosis of technical processes," in Proc. 13th IFAC Triennial World Congr., vol. 7f-01, San Francisco, CA, 1996, pp. 1-12.
4. C. Heneghan, S. B. Lowen, and M. C. Teich, "Analysis of spectral and wavelet-based measures used to assess cardiac pathology," in Proc. 1999 IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Phoenix, AZ. Paper SPTM-8.2.
5. M. Baseville and V. Nikiforov, Detection of Abrupt Changes - Theory and Applications. Englewood Cliffs, NJ: Prentice-Hall, 1993, pp. 9-11.
6. C. Boiler, W. J. Staszewski, K. Worden, G. Manson, and G. R. Tomlinson, "Structure integrated sensing and related signal processing for condition-based maintenance," presented at the Symp. Condition-Based Maintenance for Highly Engineered Systems, Università degli Studi di Pisa, Pisa, Italy, Sep. 25-27, 2000.
7. M. Daigugi, O. Kudo, and T. Wada, "Wavelet-based fault detection and identification in an oil refinery," in Proc. 14th Triennial IFAC World Congr., Beijing, China, 1999, pp. 205-210. Paper P-7e-08-05.
8. G. D. Gonzalez, G. Ceballos, R. Paut, D. Miranda, and P. La Rosa, "Fault detection and identification through variance of wavelet transform of system outputs," in Recent Advances in Intelligent Systems and Signal Processing, N. E. Mastorakis, C. Manikoupoulos, G. E. Antoniou, V. M. Mladenov, and I. F. Gonos, Eds. Athens, Greece: WSEAS Press, 2003, pp. 47-53.
9. J. Q. Zhang and Y. Yan, "A wavelet-based approach to abrupt fault detection and diagnosis in sensors," IEEE Trans. Instrum. Meas., vol. 50, no. 5, pp. 1389-1396, Oct. 2001.
10. K. V. Mardia, J. T. Kent, and J. M. Bibby, Multivariate Analysis. New York: Academic, 1979, pp. 17, 31, 213-254, 300-320.
11. A. Papoulis and S. U. Pillai, Probability, Random Variables, and Stochastic Processes, 4th ed. New York: McGraw-Hill, 2002, pp. 46-71, 371-407, 399-401, 412-413, 523-532.
12. I. Daubechies, "Ten lectures on wavelets," in CBMS-NSF Regional Conf. Series Appl. Math., vol. 61, 1992, p. 24.
13. M. Unser, "Wavelet theory demystified," IEEE Trans. Signal Process., vol. 51, no. 2, pp. 470-483, Feb. 2003.
14. E. K. Lada, J.-C. Lu, and J. R. Wilson, "A wavelet-based procedure for process fault detection," IEEE Trans. Semiconduct. Manufact., vol. 15, no. 1, pp. 79-90, Feb. 2002.
15. S. Mallat, A Wavelet Tour of Signal Processing, 2nd ed. New York: Academic, 1998, p. 79.