Wavelet analysis of matching pursuit based on genetic algorithm and its application in non-stationary fault signals

The application of wavelet analysis in fault diagnosis is growing rapidly. Especially in dealing with non-stationary fault signals, wavelet transform behaves much preponderance over Fourier Transform. Of all the wavelet analysis methods, matching pursuit is a developing technique which is specially used in analyzing high non-stationary signal in recent years. But the main problem in realizing this algorithm is how to select the best parameters in the wavelet dictionary. In this paper, matching pursuit wavelet analysis based on genetic algorithm is presented. The characteristic of genetic algorithm for searching the best value in the whole domain makes it easy to complete the matching pursuit algorithm. The simulated and tested signals are analyzed using this method respectively. The result shows that this method is effective and suitable to diagnose the non-stationary faults of impact. It is shown that matching pursuit wavelet analysis has many advantages over other wavelet analysis method and has a broad application prospect in the domain of non-stationary fault diagnosis.