Bearing Fault Diagnosis Method Based on Ensemble Composite Multi-Scale Dispersion Entropy and Density Peaks Clustering

For bearing fault diagnosis, how to effectively extract informative fault information and accurately diagnose faults is still a key problem. To this end, this study proposes a novel bearing fault diagnosis approach based on ensemble composite multi-scale dispersion entropy (ECMDE), local preserving projections and density peaks clustering. Specifically, ECMDEs are developed to capture multi-scale fault features from the raw vibration signals. The goal of ECMDEs is to synthesize different kinds of composite multi-scale dispersion entropies to find more effective fault information. Subsequently, the local preserving projections method is utilized to reduce high-dimensional feature set and extract the effective fault information. Finally, the reduced features are fed into the density peaks clustering method to obtain the fault diagnosis results. Two experimental cases and extensive comparisons are applied to validate the effectiveness and noise robustness of the proposed method. Experimental results demonstrate that the proposed method is capable to reliably extract effective fault information of raw vibration signals and accurately diagnose bearing faults even under low signal-to-noise ratio conditions.