Multivariate quality process out-of-control signal diagnostic method based on support vector machine and genetic optimization

Abstract

The invention discloses a multivariate quality process out-of-control signal diagnostic method based on a support vector machine and genetic optimization. The multivariate quality process out-of-control signal diagnostic method based on the support vector machine and the genetic optimization is characterized in that first, the types of signals likely to lead to abnormality of a multivariate process are determined according to the mean value dimensions of the multivariate process, namely the structure of a classifier model is determined; second, radial basis function parameters and penalty factors of the support vector machine are optimized with the genetic algorithm; third, the optimal support vector machine classifier model is obtained through the acquired optimal parameters, and the multivariate process out-of-control signals are diagnosed on the basis of the optimal support vector machine classifier model. The parameters of the SVM are selected dynamically through global searching ability of the genetic algorithm, and thus automatic optimization selection of the parameters of the SVM classifier is achieved, and quality diagnosis effects of the multivariate process are also promoted. The multivariate quality process out-of-control signal diagnostic method based on the support vector machine and the genetic optimization integrates the GA global searching ability and the classifying ability of the SVM, and meanwhile avoids complex calculation, simplifies the network structure of the classifier and promotes generalization ability and identification efficiency of the classifier.

Description

technical field [0001] The invention belongs to the technical field of quality engineering, and in particular relates to a method for diagnosing out-of-control signals of multivariate quality processes based on support vector machines and genetic optimization. Background technique [0002] In a multivariate industrial process, when T 2 When a control chart sends an alarm signal, it does not tell which variable or combination of variables is out of control. At present, the diagnosis of multivariable abnormal signals has become a research hotspot in multivariable process control. Hotelling was the first to realize that univariate control charts tend to ignore the correlation between variables, and proposed Hotelling statistics to monitor multivariate quality characteristics. In recent years, multivariate process quality diagnosis methods based on data mining have been intensively studied. Literature [1] (Du Fuzhou, Tang Xiaoqing. Research on multivariate quality control and...

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However, since each principal component may contain multiple original var

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