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

作者：Zhu, Xingtong; Xiong, Jianbin^*; Liang, Qiong

来源：IEEE Access, 2018, 6: 33583-33588.

DOI：10.1109/ACCESS.2018.2789933

• Fault diagnosis
• quantum genetic algorithm
• support vector machine

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.

• 出版日期2018
• 单位广东石油化工学院; 广东技术师范大学; 广东工业大学