Control chart pattern recognition using the convolutional neural network

2019 ◽  
Vol 31 (3) ◽  
pp. 703-716 ◽  
Author(s):  
Tao Zan ◽  
Zhihao Liu ◽  
Hui Wang ◽  
Min Wang ◽  
Xiangsheng Gao
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Control Chart ◽  
Control Chart Pattern

scholarly journals End-to-End Control Chart Pattern Classification Using a 1D Convolutional Neural Network and Transfer Learning

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1484
Author(s):  
Chuen-Sheng Cheng ◽  
Ying Ho ◽  
Tzu-Cheng Chiu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Process Control ◽  
Convolutional Neural Network ◽  
Statistical Process Control ◽  
Control Chart ◽  
Statistical Process ◽  
Random Patterns ◽  
Control Chart Pattern

Control charts are an important tool in statistical process control (SPC). They have been commonly used for monitoring process variation in many industries. Recognition of non-random patterns is an important task in SPC. The presence of non-random patterns implies that a process is affected by certain assignable causes, and some corrective actions should be taken. In recent years, a great deal of research has been devoted to the application of machine learning (ML) based approaches to control chart pattern recognition (CCPR). However, there are some gaps that hinder the application of the CCPR methods in practice. In this study, we applied a control chart pattern recognition method based on an end-to-end one-dimensional convolutional neural network (1D CNN) model. We proposed some methods to generate datasets with high intra-class diversity aiming to create a robust classification model. To address the data scarcity issue, some data augmentation operations suitable for CCPR were proposed. This study also investigated the usefulness of transfer learning techniques for the CCPR task. The pre-trained model using normally distributed data was used as a starting point and fine-tuned on the unknown non-normal data. The performance of the proposed approach was evaluated by real-world data and simulation experiments. Experimental results indicate that our proposed method outperforms the traditional machine learning methods and could be a promising tool to effectively classify control chart patterns. The results and findings of this study are crucial for the further realization of smart statistical process control.

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

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1472
Author(s):  
Tao Zan ◽  
Zifeng Su ◽  
Zhihao Liu ◽  
Deyin Chen ◽  
Min Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Control Chart ◽  
Information Fusion ◽  
Control Charts ◽  
Size Control ◽  
Window Size ◽  
Recognition Method ◽  
Simulation Experiments

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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