LAUSR

The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features, making it difficult to identify the fault characteristic frequency. To remove the time-varying behavior caused by speed fluctuation, the phase function of target component is necessary. However, the frequency components induced by different faults interfere with each other. More importantly, the complex sideband clusters around the characteristic frequency further hinder the spectrum interpretation. As such, we propose a demodulation spectrum analysis method for multi-fault bearing detection via chirplet path pursuit. First, the envelope signal is obtained by applying Hilbert transform to the raw signal. Second, the characteristic frequency is extracted via chirplet path pursuit, and the other underlying components are calculated by the characteristic coefficient. Then, the energy factors of all components are determined according to the time-varying behavior of instantaneous frequency. Next, the final demodulated signal is obtained by iteratively applying generalized demodulation with tunable E-factor and then the band pass filter is designed to separate the demodulated component. Finally, the fault pattern can be identified by matching the prominent peaks in the demodulation spectrum with the theoretical characteristic frequencies. The method is validated by simulated and experimental signals.