Rolling Bearing Fault Feature Extraction Using Chirplet Decomposition Based on Genetic Algorithm

Abstract

Vibration signals acquired from rolling bearing usually are complex, and it is difficult to extract fault features from strong noise background. In this paper, a chirplet decomposition method based on genetic algorithm is proposed. The absolute value of the inner product of the vibration signal and the basis function of chirplet is constructed as the optimization object function, using the genetic algorithm to search the chirplet which is best matched with the analyzed signal. Then a series of linear combination of chirplet are obtained, by which the time-frequency domain characteristic of the analyzed signal are indicated. The results confirm that the chirplet based on the genetic algorithm is more effective in extracting fault feature from strong noise background than the adaptive chirplet.