Research on fault diagnosis method of turbocharger rotor based on Hu-SVM-RFE

Abstract

Abstract Several parameters need to be monitored for turbocharger rotor faults and the overlap between different fault parameters as well as the redundancy of data, which leads to increased calculation time and reduced classification accuracy. To improve the recognition rate of turbocharger rotor faults, a recursive elimination method based on the support vector machine-recursive feature elimination (SVM-RFE) combined with improved Hu invariant moments is developed for the axial orbit feature extraction of turbocharger rotor with rotor fault. Firstly, improved Hu-invariant moments are extracted for different rotor fault axis orbits, and then the feature ranking and selection are performed by the SVM-RFE method to filter out the feature combinations with higher classification recognition rates. Then, the feature matrix of the Hu-SVM-RFE algorithm screening combination was identified for classification using each of the three diagnostic algorithms. The results show that the optimal feature subset obtained by the Hu-SVM-RFE method can ensure the richness of the fault information of the turbocharger rotor with small number of features. And, a high classification rate can be obtained with low time consumption in combination with the probabilistic neural network (PNN) algorithm. Therefore, Hu-SVM-RFE feature screening method combined with PNN fault diagnosis technology has high accuracy and efficiency, which is of great significance for online fault identification of the supercharger rotor.