Induction Motor Fault Classification Based on FCBF-PSO Feature Selection Method

Abstract

This study proposes a fast correlation-based filter with particle-swarm optimization method. In FCBF–PSO, the weights of the features selected by the fast correlation-based filter are optimized and combined with backpropagation neural network as a classifier to identify the faults of induction motors. Three significant parts were applied to support the FCBF–PSO. First, Hilbert–Huang transforms were used to analyze the current signals of motor normal, bearing damage, broken rotor bars and short circuits in stator windings. Second, ReliefF, symmetrical uncertainty and FCBF three feature-selection methods were applied to select the important features after the feature was captured. Moreover, the accuracy comparison was performed. Third, particle-swarm optimization (PSO) was combined to optimize the selected feature weights which were used to obtain the best solution. The results showed excellent performance of the FCBF–PSO for the induction motor fault classification such as had fewer feature numbers and better identification ability. In addition, the analyzed of the induction motor fault in this study was applied with the different operating environments, namely, SNR = 40 dB, SNR = 30 dB and SNR = 20 dB. The FCBF–PSO proposed by this research could also get the higher accuracy than typical feature-selection methods of ReliefF, SU and FCBF.

Keywords

• fast correlation-based filter (FCBF)
• back propagation neural network (BPNN)
• Hilbert–Huang transform (HHT)
• motor failure
• ReliefF
• symmetrical uncertainty (SU)