Fault diagnosis based on misclassification loss minimized SVM

In order to solve the classification bias problem in fault diagnosis based on the decision directed acyclic graph support vector machine (DAG-SVM), a misclassification loss minimized SVM (MLM-SVM) is proposed to optimize the multi-type decision structures. Compared with conventional methods which are aimed to maximize the diagnosing accuracy, this approach takes the different losses brought by different misclassifications into consideration and sets the minimization of misclassification losses as the goal for optimization. Dealing with the k-type fault diagnosis, the MLM-SVM first gives the penalty factors for all misclassification cases, and generalizes the misclassification loss confusion matrixes for all k! decision structures. Then, the misclassification loss confusion matrixes and the risk function for total losses are combined, and the misclassification losses for all corresponding decision structures are obtained. Furthermore the decision structure with the smallest misclassification loss for fault diagnosis is obtained. The approach is applied to the transformer fault diagnosis and the best structure is obtained. Then, all the k! decision structures are made for diagnosis and the corresponding misclassification losses are calculated to obtain the best structure. The two results are consistent, indicating the effectiveness of the proposed approach.