Support Vector Machine Detection Strategies for Different Types of Faults

In this study, the application of support vector machine (SVM) in modern industrial fault diagnosis is mainly analyzed. As an advanced machine learning algorithm, SVM can effectively distinguish normal and abnormal states by constructing optimal classification hyperplanes and maximizing classification intervals. Especially when dealing with faults F1 and F5 with complex features, the effectiveness and potential of SVM technology are proven, thereby improving the accuracy and efficiency of fault detection. The results of this paper show that SVM achieves an accuracy of 93.85% in fault F1 detection. Additionally, an accuracy of 99.68% is achieved in the more challenging fault F5 detection. This efficient diagnostic ability is attributed to the kernel function technology of SVM, which can process nonlinear features and map them to higher-dimensional space to identify complex fault modes. However, this study also points out the limitations of SVM in feature extraction and model training. Especially in the diagnosis of fault F1, the inability to fully extract effective features will lead to certain classification errors. In future work, more in-depth research on optimal feature selection, kernel function selection, and parameter tuning is needed to further improve the performance of SVM in industrial fault diagnosis applications. Overall, the SVM studied in this paper shows great potential in the field of industrial automation fault diagnosis. It provides valuable practical experience and theoretical basis for the development of related technologies in the future.