Research on tacholess fast-varying instantaneous rotating frequency estimation model based on nonlinear short-time Fourier transform combination with adaptive chirp mode decomposition and its application

Instantaneous rotating frequency extraction is one of the key technologies for mechanical health monitoring and fault diagnosis. As the instantaneous rotating frequency presents fast-varying property under high-speed fluctuation, this paper uses a coarse-to-fine strategy to propose a tacholess fast-varying instantaneous rotating frequency estimation model based on nonlinear short-time Fourier transform (NLSTFT) combination with adaptive chirp mode decomposition (ACMD). By self-adaptive matching and decomposing the vibration signal based on its time-frequency distribution, it increases the energy aggregation of time-frequency representation, which not only improves computational efficiency but also avoids the spectral ambiguity problem. As a result, the proposed model is very suitable for extracting instantaneous rotating frequency under severe speed fluctuation; simulation signal and rolling bearing fault vibration signal also validate this conclusion. Furthermore, by integrating with signal decomposition technology, various order components of fault vibration signal can also be self-adaptive extracted.