Roller Bearing Fault Diagnosis Based on Empirical Mode Decomposition and Targeting Feature Selection

Abstract

At present, the feature extraction of frequency signal based on empirical mode decomposition (EMD) has been widely studied and applied in fault diagnosis of rolling bearings. However, there are still some shortcomings in fault diagnosis based on EMD. Therefore, a fault diagnosis method based on the combination of EMD and target feature selection (TFS) is proposed in this paper. The method firstly analyzes the fault signal through EMD and extracts the fault features. Then, it removes the redundant features and optimizes the feature subsets by using TFS. TFS selects the most effective feature for each target sample space through filtering evaluation criteria and heuristic search strategy, thereby effectively improving the accuracy and efficiency of fault diagnosis.