Feature extraction by enhanced time-frequency analysis method based on Vold-Kalman filter

The time-frequency analysis method can extend a one-dimensional signal to a two-dimensional time-frequency plane, revealing the signal's time-varying characteristics. The time-frequency representation (TFR) obtained by the time-frequency postprocessing algorithm has the characteristics of energy aggregation and high resolution. The generalized S-synchroextracting transform (GS-SET) stands out for its strong adaptability. However, this method cannot obtain effective information when analyzing multicomponent complex signals. We propose an enhanced time-frequency analysis method to solve this problem. First, the multicomponent complex signal is decomposed into multiple mono-component signals by the Vold-Kalman time-varying filtering technique. Second, these signals are processed by the GS-SET method. Last, the obtained TFRs are linearly superimposed to obtain the results of the enhanced method. The simulated signal verifies that the proposed method can effectively represent its time-varying characteristics. The experimental signal of the rolling bearing verifies the practicability of this method.