Research on Fault Diagnosis Based on SVM and Monkey-King Genetic Algorithm

In the fault diagnosis based on support vector machine (SVM), SVM parameters are mostly selected artificially or obtained through experiment time after time, a certain and effective method has not been found. Aiming at this problem, a method optimizing the SVM parameters with Monkey-King genetic algorithm (MKSVM) is presented. In the built model the optimized parameters are used, and the superiority of SVM in processing finite samples is fully brought into play. The experimental result shows that the method can obtain higher diagnosis accuracy with fewer feature and the proposed method can find out the optimum accurately in a wide range and the value can be used to diagnose the fault effectively.