Fault diagnosis of bearings in multiple working conditions based on adaptive time-varying parameters short-time Fourier synchronous squeeze transform

Rolling bearings are commonly used components in rotating machinery and play a vital role. When the bearing fails, if it cannot be found and repaired in time, it will cause great economic losses. Time-frequency analysis has been widely used for bearing fault signals under non-stationary operating conditions, but the existing methods have problems such as poor adaptability under multiple operating conditions. At the same time, the low time-frequency resolution and poor energy aggregation also affect the fault feature extraction effect. Aiming at these problems, this paper proposes a bearing fault detection method, which combines empirical mode decomposition and adaptive time-varying parameter short-time Fourier synchronous squeezing transform (AFSST), it solves the problem of adapting to signals under multiple operating conditions; A weighted least squares estimation time-varying parameter algorithm is proposed, which improves the calculation speed by 29% under the premise of ensuring the calculation accuracy; A time-varying index of energy effective compression ratio is proposed to accurately measure the time-varying energy aggregation of time-frequency analysis methods. Using short-time Fourier transform, continuous wavelet transform, wavelet synchrosqueezed transform, and AFSST to analyze the simulated FM signal, the results show that the AFSST transform has better time-frequency resolution and higher energy-efficient compression rate globally. Through the verification of the fault experimental data of rolling bearings, the diagnosis method proposed in this paper can accurately extract the bearing fault characteristics, has a good diagnosis ability in the multi-working operating environment, and has strong robustness and anti-noise interference.