A robust fault isolation method based on DTW
Fault Detection, Supervision and Safety of Technical Processes, Volume # 6 | Part# 1
Authors
Zheng Niu; Yuguang Niu; Yurong Li; Jizhen Liu
Digital Object Identifier (DOI)
10.3182/20060829-4-CN-2909.00234
Page Numbers:
1402-1406
Index Terms
fault isolation,dynamic time warping,comparability measure,robustness
Abstract
The precise mathematical model of industrial process is difficult to be obtained, so a fault isolation method independent of process mathematical model is proposed. This method is based on dynamic time warping (DTW) technology and isolates process fault by process data analysis . First, the fault pattern storehouse is established through history data analysis and process knowledge. Then the detection sample is carried on pattern matching with each fault sample in fault pattern storehouse by DTW. Finally, the matching results are quantitatively evaluated to isolate fault through comparability measure. The simulation result of three-tank-system indicates that this method has good robustness to the process time -varying characteristic, thus has high fault isolation precision.
References
[1] Bo Xu, Hailong Tang and Xingshan Li (2004). DTW
based quantitative fault diagnosis of gas path
component in turbofan. Journal of Beijing
University of Aeronautics and Astronautics,
30(6), 524-528.
[2] Chiang L. H., Russell E. L. and Braatz R. D. (2001).
Fault detection and diagnosis in industrial
systems. Springer, London.
[3] Donghua Zhou and Yinzhong Ye (2000). In: Modern
fault diagnosis and fault tolerant control.
Tsinghua University Press, Beijing.
[4] Gertler J. J. (1998). Fault detection and diagnosis in
engineering systems. Marcel Dekker, Inc., New
York.
[5] Isermann R. (1995). Model based fault detection and
diagnosis methods. Process of the American
Control Conference, New Jersey, pp. 1605-1609.
[6] Isermann R. and Freyermuth B. (1991). Process fault
diagnosis based on process model knowledge-Part
I: Principles for fault diagnosis with
parameter estimation. Journal of Dynamic
Systems, Measurement and Control, 113(4),
620-626.
[7] Keogh E. J. and Pazzani M. J. (2000). Scaling up
dynamic time warping for data mining
application. Proceedings of the 6th International
Conference on Knowledge Discovery and Data
Mining, Boston, pp. 285-289.
[8] Scinivasan A. and Batur C. (1994). Fault detection
and isolation in an unsupervised learning
environment. Pattern Recognition Letters, 15(3),
235-242.
[9] Xiaobin Huang, Yuguang Niu, Jizhen Liu and
Yuanzhi Sun (2003). Fault diagnosis for sensors
based on fuzzy dynamical model. Proceedings of
the CSEE, 23(3), 183-187.
[10] Yong Chen and Jun Liang (2002). Monitoring and
fault diagnosis based on PCA for multivariable
control system. Journal of Engineering Design,
9(5), 257-260.
[11] Zhang J., Martin E. and Morris A. J. (1995). Fault
detection and classification through multivariate
statistical techniques. Process of the American
Control Conference, New Jersey, pp. 751-755.
[12] Zheng Niu, Jizhen Liu and Yuguang Niu (2005).
Fault detection under varying load conditions
based on dynamic multi-principal component
models. Chinese Journal of Power Engineering,
25(4), 554-558.
