Fault Diagnosis of Subway Plug Door based on EEMD and Adaptive Feature Extraction

Fault diagnosis of the subway plug door plays an essential role in the safe operation of the city subway. To improve the diagnosis accuracy of the subway plug door, fault diagnosis of the plug door based on Ensemble Empirical Mode Decomposition (EEMD) and adaptive feature extraction was presented in this paper. Firstly, EEMD was used for decomposition of raw data, and the intrinsic mode function (IMF) after decomposition was selected by correlation coefficient criteria. Then, the fault features in IMFs was extracted and the sensitive features among which was selected by the sensitive index. Finally, the faults were classfied by Gray Wolf optimized Support Vector Machine (GWO-SVM). The experiment with the measured data of a subway door shows that this fault diagnosis method can adaptively extract the relative optimal characteristic quantity, identify the normal state and four different fault states effectively with the recognition accuracy of 89.35%, which is valuable in the engineering application.