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This paper studies the quality pattern recognition of industrial process based on the statistical process control (SPC). An abnormal quality pattern recognition model based on multi-support vector machine was proposed, which can be used to solve the problem of abnormal pattern recognition in the intelligent manufacturing process for products. The combination of "one-to-one" and "one-to-many" support vector machine (SVM) classifiers is arranged according to the structure of directed acyclic graphs in the model. At the same time, a structural optimization method was proposed to reduce the cumulative error problem. The model uses the original features of the data stream of quality. For the support vector machine classifier with low recognition accuracy, the statistical features and shape features form the data stream of quality are integrated with the original features. Relief algorithm is used to reduce the fusion features in order to reduce the consumption caused by increased features. The experimental results demonstrate that the model improves the accuracy of the recognition of abnormal patterns, and its structure also has a good time advantage.

Abnormal Quality Pattern Recognition of Industrial Process Based on Multi-Support Vector Machine