Fault diagnosis of sensor pulse signals based on improved energy fluctuation index and VMD

Variational mode decomposition (VMD) has been widely applied in sensors. However, the mode number and balance parameter seriously limit VMD application. To solve this problem, this study proposes a novel method, which combines an improved energy fluctuation index (IEFI) and modified VMD (MVMD). In the proposed method, IEFI provided better performance to resist interference from random impulses by considering the periodicity of fault feature components. Consequently, it is applied to determine the initial center frequency for MVMD, which fixed the problem of the mode number. Moreover, a novel balance parameter search strategy, which can adaptively determine the optimal balance parameter, is combined with MVMD whose stop condition is replaced by kurtosis to extract the fault feature. Simulation results indicated that the proposed method does well in detecting the feature of a periodic impulse signal from the signal polluted by some interference impulses. Moreover, the bearing fault diagnosis results demonstrate that the proposed method can accurately detect bearing fault features. Furthermore, the method was validated with bearing fault data. The results showed that the method can accurately extract the fault characteristics of the impulse signal and achieve fault diagnosis.

Automated summary

This study proposes a novel method that combines an improved energy fluctuation index (IEFI) and modified VMD (MVMD) to solve the issues of mode number and balance parameter in VMD application. Simulation results show that the proposed method can effectively detect the feature of a periodic impulse signal and accurately diagnose bearing faults.