Fault Diagnosis of Rotation Machinery Based on Support Vector Machine Optimized by Quantum Genetic Algorithm

Abstract

Considering the disadvantages of conventional fault diagnosis methods for rotating machinery, such as low efficiency and low accuracy, we propose a fault diagnosis method based on support vector machine (SVM) optimized by quantum genetic algorithm (QGA). First, the SVM parameters are optimized by QGA. Then, the training data set is used to train the SVM model, and the test data set is used for model testing. The experimental results show that the proposed method has higher accuracy in fault diagnosis than traditional SVM method and the method based on genetic algorithms and SVM.