Hydraulic System Faults Diagnosis Based on Multi-class Support Vector Machine

There is no sufficient evidence on classification, because of lack of hydraulic system fault samples. The classification results with definite guess are not exactly right. Meanwhile, there are many types of hydraulic system faults, but present classifiers can only classify two-class problems, which are not fit for hydraulic system faults diagnosis. In order to solve the preceding problems, a method for hydraulic system faults diagnosis based on multi-class support vector machine (MSVM) is proposed. A support vector machine (SVM) has strong classification ability with fewer samples taker. For k -class problem of hydraulic system, it combines k(k-1)/2 two-class SVM classifiers, one for each pair of classes. The experimental results indicate that this method is a more effective and feasible tool for hydraulic system faults diagnosis than Neural Net.