Adaptive Chirplet Decomposition Method and Its Application in Machine Fault Diagnosis

We propose a new approach based upon the adaptive chirplet decomposition to characterize the time-dependent behavior of machine vibration signals. This approach employs maximum projective decomposition algorithm combined fractional Fourier transform with quasi-Newton method to estimate the parameters. Then, the expectation maximization algorithm is used to refine the results. Compared with traditional time-frequency analysis methods, such as short-time Fourier spectrum and Wigner distribution, the simulation results show that this method can obtain more accurate estimation, finer time-frequency resolution and de-noising capability. Finally, the proposed method is applied to the fault diagnosis of bearing, and the results of the experiment demonstrate that the proposed method is efficient in signal feature extraction.