Toward efficient and accurate extraction of instantaneous frequency with chirplet transform and its applications in bearing fault diagnosis

In many engineering applications, the instantaneous frequency (IF) needs to be identified rapidly and precisely, especially in rotating machinery condition monitoring, where quasi-real-time analysis is a favorable option, and any error in shaft speed (identified from the instantaneous frequency) needs to be reduced to prevent it from accumulating with time. The modern time–frequency analysis (TFA) method provides a way for IF identification; however, sometimes, it still suffers from energy smearing and closely spaced frequency interference. In addition, many TFA methods based on the chirplet transform also present the problem of excessive computation when searching for the optimal parameters. To overcome these limitations, we propose the multi-extracting velocity-synchronous chirplet transform (MEVCT), an enhanced TFA method, in this paper. In the MEVCT, a refined chirp parameter search is developed first to reduce the computational cost while increasing time–frequency accuracy. Then, an iterative high-order IF estimator is constructed to allow the IF to achieve or approach the actual values even in highly nonlinear situations. After that, the obtained IF estimate is substituted into the simultaneous extraction operator to reduce energy dispersion further and eliminate non-reassigned points (NRPs). Simulated and experimental signal analyses demonstrate that MEVCT is able to eliminate NRPs completely. Furthermore, when handling synchronous multicomponent signals, the proposed method has higher IF accuracy with less computational cost and better anti-noise capability.