Enhancement of time-frequency post-processing readability for nonstationary signal analysis of rotating machinery: Principle and validation

Rotating machinery signals are often composed of multiple components and are nonstationary under practical time-varying conditions. In most cases, the constituent frequency components are close to each other on time-frequency plane. As such, better time-frequency readability is necessary to discover the underlying physical nature of such complex nonstationary signals. Nevertheless, conventional time-frequency analysis methods suffer from limited time-frequency resolution and/or cross-term interferences, thus cannot achieve desirable time-frequency read-ability. In recent decades, some time-frequency post-processing methods (such as time-frequency reassignment, synchrosqueezing transform, multi-tapering, concentration of frequency and time, and higher-order synchrosqueezing transform) have been proposed to improve the time--frequency readability from two aspects: reducing cross-term interferences and enhancing time-frequency energy concentration. However, they still surfer from time-frequency blurs for the signal whose time-frequency ridges are close to each other on time-frequency plane. To address this issue, we propose a framework to improve the post-processing methods. Firstly, the Vold-Kalman filter is employed to extract constituent frequency components, by exploiting its capa-bility to separate mono-components. Then, the time-frequency representation (TFR) of each mono-component is obtained via time-frequency post-processing method separately. Finally, the TFR of raw signal is constructed by superposing the TFR of all mono-components. This framework achieves a TFR of high time-frequency resolution, free from cross-term interferences, and therefore better time-frequency readability. As such, it extends time-frequency post-processing methods to analyze complex nonstationary signals. The proposed framework is illustrated through numerical simulation, and validated using typical rotating machine vibration data (including the experimental signals of a planetary gearbox and an induction motor, ground test data of a civil aircraft engine and in-situ measurement of a hydraulic turbine rotor). The analysis results show the good capability of proposed framework to reveal the frequency contents and their time-varying features of complex nonstationary signals.