Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition

This paper develops a novel dominant feature selection method using a genetic algorithm with a dynamic searching strategy. It is applied in the search for the most representative features in rotary mechanical fault diagnosis, and is shown to improve the classification performance with fewer features. First, empirical mode decomposition (EMD) is employed to decompose a vibration signal into intrinsic mode functions (IMFs) which represent the signal characteristic with sample oscillatory modes. Then, a modified genetic algorithm with variable-range encoding and dynamic searching strategy is used to establish relationships between optimized feature subsets and the classification performance. Next, a statistical model that uses receiver operating characteristic (ROC) is developed to select dominant features. Finally, support vector machine (SVM) is used to classify different fault patterns. Two real-world problems, rotor-unbalance vibration and bearing corrosion, are employed to evaluate the proposed feature selection scheme and fault diagnosis system. Statistical results obtained by analyzing the two problems, and comparative studies with five well-known feature selection techniques, demonstrate that the method developed in this paper can achieve improvements in identification accuracy with lower feature dimensionality. In addition, the results indicate that the proposed method is a promising tool to select dominant features in rotary machinery fault diagnosis.