Fault Diagnosis of AC Servo Motor with Current Signals Based on Wavelet Decomposition and Template Matching Methods

This paper presents a diagnosis technique to detect and identify faults in AC servo motors. The first phase of stator currents among three phases is digitized and stored in the time domain. Wavelet transform is employed to convert the signals onto time-frequency domain because the time domain based approach is not suitable for detecting state features of the current signals. Pre-processing algorithms that includes a kind of mean-filtering, synchronization with Hilbert transform and difference are consecutively performed to the raw signal to determinate features. Wavelet decomposition is applied to the difference values by the optimally selected mother wavelet and the features are calculated from the transformed signals. The extracted features are compared with the motor fault templates for the template matching method. The results based on real data show that the proposed approach is very useful to extract features of the signals for fault diagnosis.