Quality control chart pattern recognition method based on improved genetic algorithm optimization

Abstract

The invention provides a quality control chart pattern recognition method based on improved genetic algorithm optimization. The quality control chart pattern recognition method comprises the followingsteps: simulating various pattern characteristics of a control chart by using a Monte Carlo method; generating data of a corresponding mode through the parameter values; adopting the PCA principal component analysis method to carry out dimension reduction and denoising on the original data, main features of the data are extracted, shortening the training time of the model and improvintg the recognition accuracy; establishing a probabilistic neural network model, and carrying out pattern classification recognition by utilizing the characteristics of simple structure and convenient training; optimizing a main parameter smoothing factor of the probabilistic neural network by virtue of an improved single-objective optimization genetic algorithm; searching possible abnormal reasons from different aspects according to the identification result.The method solves the problems that all abnormal conditions cannot be monitored and recognized when an existing enterprise carries out quality control, effective abnormal information is difficult to find from a control chart, and appropriate measures cannot be taken to correct the abnormal conditions in the production process.

Description

technical field [0001] The invention belongs to the field of quality state monitoring, in particular to a quality control chart pattern recognition method optimized based on an improved genetic algorithm. Background technique [0002] The manufacturing process of products is a complex nonlinear process affected by various factors such as personnel, equipment and materials. At present, the main tool for monitoring and quality control of production is SPC control chart. The pattern recognition of the control chart is of great significance for timely detection of production abnormalities and avoiding quality loss. Most of the current pattern recognition methods for control charts are based on BP neural network or SVM support vector machine. The training speed of BP neural network is slow, and the structure design needs to rely on personal experience, which is not only time-consuming and inefficient, but also the recognition effect is not ideal; SVM is a binary classifier, and ...

AI Technical Summary

Problems solved by technology

The training speed of BP neural network is slow, and the structure design needs to rely on personal experience, which is not only time-consuming and inefficient, but also the recognition effect is not ideal; SVM is a binary classifier, and control chart pattern recognition is a multi-classification problem, so it is necessary to construct and train multiple Only one SVM model can fully recognize all modes, and it is more complicated in the face of mixed modes.

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