Abnormal Detection of Mechanical Automation Production Process Based on Support Vector Machine

The purpose of this paper is to explore and implement an effective anomaly detection framework to improve the stability and reliability of mechanical automation production process. Support vector machine (SVM) is adopted as the core algorithm. By constructing an anomaly detection model for high-dimensional and complex data, the potential anomalies in the production process can be identified in time. In the experimental stage, firstly, a data set containing normal and abnormal data is synthesized, covering the measured values of several key sensors. By dividing the data set into training set and test set, an SVM model using radial basis function is trained. The evaluation results on the test set show that the model achieves 90% accuracy, 92% accuracy and 88% recall rate, which verifies the superior performance of SVM in abnormal detection of mechanical automation production process. The experimental results further demonstrate the excellent performance of SVM in processing high-dimensional data, capturing nonlinear relationships and multi-category problems. It successfully adapts to different types of abnormal situations through the introduction of kernel function, and provides a comprehensive abnormal detection ability for the production process. This study provides a feasible solution for abnormal detection of mechanical automation production process, and provides a useful reference for manufacturing industry to realize intelligent monitoring and management of production system.