An enhanced spline-based time-varying filtering method and fast MKurtgram for bearing fault identification under random impulsive environment

In certain engineering problems, the presence of random impulsive components can heavily corrupt a signal. Thus, random impulsive components are also known as random impulsive noise or non-Gaussian noise. Commonly used signal processing tools in fault diagnosis may perform poorly under such operational conditions. To this end, an alternative approach integrating time-varying filtering with fast MKurtgram (FMK) is proposed as a solution. The FMK is developed based on the fast kurtogram (FK). The FMK addresses a major drawback of the FK, which is vulnerable to random impulsive noise, thus FMK exhibits good performance in impulsive environments. However, the FMK may produce inaccurate estimates of the bandwidth and center frequency when random impulsive noise is relatively intensive. To alleviate this, a spline-based time-varying filtering (TVF) method is designed in this work. This spline-based TVF method is highly effective in eliminating a large amount of Gaussian noise, as well as attenuating the energy of the impulsive components. After undergoing this pre-treatment, the FMK can estimate both bandwidth and center frequency more precisely. The proposed approach is demonstrated to be effective through the simulated and real bearing fault signals.