Articlegold2020

Pattern Recognition of Different Window Size Control Charts Based on Convolutional Neural Network and Information Fusion

Tao Zan · Zifeng Su · Zhihao Liu · Deying Chen · Min Wang · Xiangsheng Gao

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Advanced Statistical Process Monitoring Fault Detection and Control Systems Industrial Vision Systems and Defect Detection

Abstract

Control charts are an important tool for statistical process control (SPC). SPC has the characteristics of fluctuation and asymmetry in the symmetrical coordinate system. It is a graph with control limits used to analyze and judge whether the process is in a stable state. Its fast and accurate identification is of great significance to the actual production. The existing control chart pattern recognition (CCPR) method can only recognize a control chart with fixed window size, but cannot adjust with different window sizes according to the actual production needs. In order to solve these problems and improve the quality management effect in the manufacturing process, a new CCPR method is proposed based on convolutional neural network (CNN) and information fusion. After undergoing feature learning, CNN is used to extract the best feature set from the control chart, while at the same time, expert features (including one shape features and four statistical features) are fused to complete the CCPR. In this paper, the control charts of 10 different window sizes are generated by the Monte Carlo simulation method, and various data patterns are drawn into images, then the CCPR model is set up. Finally, simulation experiments and a real example are addressed to validate its feasibility and effectiveness. The results of simulation experiments demonstrate that the recognition method based on CNN can be used for pattern recognition for different window size control charts, and its recognition accuracy is higher than the traditional ones. In addition, the recognition method based on information fusion performs much better. The result of a real example shows that the method has potential application in solving the pattern recognition problem of control charts with different window sizes.

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References

1992Introduction to Statistical Quality Control 2003Improved SPC chart pattern recognition using statistical features 2012A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme 1994Control chart pattern recognition using learning vector quantization networks 1999A neural network based model for abnormal pattern recognition of control charts 2019Control chart pattern recognition using the convolutional neural network 2018Control chart pattern recognition using RBF neural network with new training algorithm and practical features 2008Recognition of control chart patterns using improved selection of features

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