Solenoid Valve Fault Diagnosis Based on Genetic Optimization MKSVM

2nd International Symposium on Resource Exploration and Environmental Science

生态环境科学

Guo, Wenhao^1 ; Cheng, Jinjun^1 ; Tan, Yangbo^1 ; Liu, Qiang^1

College of Aeronautics Engineering, Air Force Engineering University, Xi'an

710038, China^1

关键词: Diagnosis methods;    Empirical Mode Decomposition;    Fault diagnosis method;    Genetic optimization;    Kernel parameter;    Multi-kernel learning;    Multiple Kernel Learning;    Weight coefficients;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/170/4/042134/pdf
DOI  :  10.1088/1755-1315/170/4/042134

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

In order to realize the accurate diagnosis of the solenoid valve fault and the accurate identification of the fault type, a fault diagnosis method of solenoid valve based on MKSVM (Multi-Kernel Support Vector Machine) is proposed. Firstly, through the analysis of support vector machine theory, a multiple-kernel learning support vector machine model is built, and the genetic algorithm is used to optimize the multiple-kernel learning weight coefficient and kernel parameter configuration. Then, the current signal of the solenoid valve driving terminal under the six common failure modes of the solenoid valve is obtained experimentally, and the characteristic information is extracted by EMD (Empirical Mode Decomposition) based on the current change rate. Finally, the multi-kernel learning SVM model was used to diagnose the state of the solenoid valve corresponding to each data, and its accuracy rate reached 98.9%. The comparison with the single-core diagnosis method shows that the method can accurately detect the solenoid valve fault Diagnosis, for similar studies with reference value.