Application of acoustic emission technique in diagnosis of early rolling bearing faults

In this paper, the early fault acoustic emission signals of rolling bearing are investigated by means of continuous wavelet transform. Morlet wavelet is selected and the wavelet parameters are optimized based on the principle of minimal wavelet entropy. A new method termed as generalized soft-threshold de-noising is used to filter the wavelet transform coefficients. The de-noised signals obtained by reconstructing the wavelet coefficients contain the obvious impulsive characteristics, and the frequency of the impulse interval is equal to the rolling bearing fault characteristic frequency. Based on the optimized waveform parameters, the wavelet scalogram is used to analyze the real AE signal of rolling bearing in experimental test rig. The results indicate that the proposed method is useful and efficient.