Fault feature extracting by wavelet transform for control system fault detection and diagnosis

In this paper, to overcome the difficulty in fault feature extracting from the residual in model-based method for control system fault detection and diagnosis, based on the fact that the wavelet transform of a signal will take its modulus maximum at its singular point in transform domain, and the fault error has positive singularity exponent but noise has negative singularity exponent at the corresponding singular points, the fault error and noise mixed in the residual can be separated from each other by multi-scale wavelet transform, and the modulus maximum can be taken as the fault feature, so that the fault feature becomes clearer and more recognizable and a correct decision whether the system fault toke place or not can be correctly made in transform domain. This makes it easy to detect and diagnose the fault in control system.