Bearing fault diagnosis under non-stationary conditions based on a speed signal resonance component demodulation algorithm

Abstract

This paper proposes a novel bearing fault diagnosis indicator, the resonance component, for motor speed signals. The speed signal was modulated into a higher-frequency band using a double-inertia system, and the bearing fault information was carried and reserved in this high-frequency resonance component. Variational mode decomposition was then used to separate the components unrelated to the resonance based on the parameters optimized using the artificial bee colony algorithm. After envelope demodulation and angular resampling, the multipoint optimal minimum entropy deconvolution adjusted algorithm was utilized to enhance the amplitude of the order spectrum at the corresponding faulty order. Additionally, experiments were successfully conducted on both the outer and inner raceway bearing faults under time-varying conditions using the proposed method. The results indicate that the ordinary diagnosis method using the basic bearing fault characteristic frequency as an indicator is less effective.

Multicomponent collaborative time-frequency state-space model for vibration signal decomposition under nonstationary conditions

“…In [19], an enhanced Vold-Kalman filter for signal decomposition was presented using separated cosine and sine kernels. VMD was used in [20] to separate the components unrelated to the resonance based on the parameters optimized using the artificial bee colony algorithm. A novel two-level chirp mode decomposition approach was proposed in [21] to decompose complex AM-FM signals with strong noise.…”