Multi-scale chirplet synchroextracting transform for accurate characterization of adjacent fault features in rotating machinery

When mechanical equipment fails, the fault characteristics are often interfered by adjacent components. Therefore, how to well characterize the time-varying laws of multi-component signals containing adjacent components has always been a difficulty and research hotspot in the application of time–frequency analysis (TFA) technologies in mechanical fault diagnosis. In this paper, a new TFA method is proposed, called the Multi-scale chirplet synchroextracting transform (MCSET). On the basis of chirplet transform (CT), by using two additional parameters, the window rotation step is divided into two sections within each window length to more accurately match the instantaneous frequency (IF) trajectory of the nonlinear frequency modulation signal. In this way, the energy-concentrated time–frequency (TF) distribution of the multi-component signal containing adjacent components can be obtained. Moreover, to further improve the TF resolution, a new frequency estimation operator is constructed using the idea of synchronous extraction to more accurately capture the IF variation law of multi-component signals and keep the energy highly concentrated. MCSET can well characterize the dynamic characteristics of components adjacent to the IF trajectory, and as a parameterized TFA technique, it maintains good noise robustness. In simulation and experiments, compared with other similar advanced TFA techniques, the results can verify the effectiveness of the proposed method and its superiority in processing complex non-stationary signals with adjacent IF trajectories.