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Support vector machine fault diagnosis based on sparse scaling convex hull

In solving the problems encountered when implementing support vector machine (SVM) fault diagnosis, the convex-hull-covering model of the dataset cannot reflect the effective distribution of the samples, and the fault identification accuracy of the original high-dimensional feature set is low. A sparse scaling convex-hull based SVM classification method is proposed and applied to the fault diagnosis of roller bearings. The dimensionality reduction of the features of the sample set is carried out by the random forest (RF) algorithm. First, the optimized sample subsets are obtained by sparse approximation, and the reduction coefficient of the convex hull of the optimized sample set is adjusted, hence the convex hulls of various sample sets are linearly separable. Second, to solve the problem of low fault recognition accuracy of the original high-dimensional feature set, the importance of features is evaluated by RF, and some redundant features are removed. Finally, the SVM model is constructed by the closest points between the convex hulls. Through fault diagnosis on two different bearing datasets, the experimental results and related theories show that the proposed method has high performance in bearing fault diagnosis.