Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

Wavelet analysis with its unique features is very suitable for analyzing non-stationary signal, and it can also be used as an ideal tool for signal processing in fault diagnosis. The characteristics of the faults and the necessary information on the diagnosis can be constructed and extracted respectively by wavelet analysis. Though wavelet analysis is specialized in characteristics extraction, it can not determine the fault type. So this paper has proposed an energy analysis method based on wavelet transform. Experiment results show the method is very effective for sensor fault diagnosis, because it can not only detect the sensor faults, but also determine the fault type.

[1]  Koichi Suyama.  Fault detection of redundant sensors used in reliable sampled-data control systems , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[2]  Ethan A. Scarl,et al.  Diagnosis and Sensor Validation through Knowledge of Structure and Function , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  E. Zafiriou,et al.  Use of neural networks for sensor failure detection in a control system , 1990, IEEE Control Systems Magazine.

[4]  Yves Meyer,et al.  Wavelets and Applications , 1992 .

[5]  Steven Reece,et al.  Multi-sensor fault recovery in the presence of known and unknown fault types , 2009, 2009 12th International Conference on Information Fusion.

[6]  John J. Deyst,et al.  Sensor Validation: A Method to Enhance the Quality of the Man/Machine Interface in Nuclear Power Stations , 1981, IEEE Transactions on Nuclear Science.

[7]  D. Fussel,et al.  Detection and isolation of sensor faults on nonlinear processes based on local linear models , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[8]  T.-H. Guo,et al.  Sensor failure detection and recovery by neural networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.