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Multiscale fuzzy entropy (MFE) has been a prevalent tool to quantify thecomplexity of time series. However, it is extremely sensitive to thepredetermined parameters and length of time series and it may yield aninaccurate estimation of entropy or cause undefined entropy when the length oftime series is too short. In this paper the Sigmoid-based refined compositemultiscale fuzzy entropy (SRCMFE) is introduced to improve the robustness ofcomplexity measurement of MFE for short time series analysis. Also SRCMFE isused to quantify the dynamical properties of mechanical vibration signals andbased on that a new rolling bearing fault diagnosis approach is proposed bycombining SRCMFE with t-distributed stochastic neighbor embedding (t-SNE) forfeature dimension and variable predictive models based class discrimination(VPMCD) for mode classification. In the proposed method, SRCMFE firstly isemployed to extract the complexity characteristic from vibration signals ofrolling bearing and t-SNE for feature dimension reduction is utilized to obtaina low dimensional manifold characteristic. Then VPMCD is employed to constructa multi-fault classifier to fulfill an automatic fault diagnosis. Finally, theproposed approach is applied to experimental data of rolling bearing and theresults indicate that the proposed method can effectively distinguish differentfault categories of rolling bearings.

Sigmoid-Based Refined Composite Multiscale Fuzzy Entropy and t-Distributed Stochastic Neighbor Embedding Based Fault Diagnosis of Rolling Bearing