Control chart pattern recognition using the convolutional neural network

  • Tao Zan
  • Zhihao LiuEmail author
  • Hui Wang
  • Min Wang
  • Xiangsheng Gao


Unnatural control chart patterns (CCPs) usually correspond to the specific factors in a manufacturing process, so the control charts have become important means of the statistical process control. Therefore, an accurate and automatic control chart pattern recognition (CCPR) is of great significance for manufacturing enterprises. In order to improve the CCPR accuracy, experts have designed various complex features, which undoubtedly increases the workload and difficulty of the quality control. To solve these problems, a CCPR method based on a one-dimensional convolutional neural network (1D-CNN) is proposed. The proposed method does not require to extract complex features manually; instead, it uses a 1D-CNN to obtain the optimal feature set from the raw data of the CCPs through the feature learning and completes the CCPR. The dataset for training and validation, containing six typical CCPs, is generated by the Monte-Carlo simulation. Then, the influence of the network structural parameters and activation functions on the recognition performance is analyzed and discussed, and some suggestions for parameter selection are given. Finally, the performance of the proposed method is compared with that of the traditional multi-layer perceptron method using the same dataset. The comparison results show that the proposed 1D-CNN method has obvious advantages in the CCPR tasks. Compared with the related literature, the features extracted by the 1D-CNN are of higher quality. Furthermore, the 1D-CNN trained with simulation dataset still perform well in recognizing the real dataset from the production environment.


Control chart Pattern recognition Convolutional neural network Feature learning Deep learning 



This study is supported by National Natural Science Foundation of China (No. 51575014), Science and Technology Project of Beijing Municipal Commission of Education (KM201410005026) and National Fund for Studying Abroad (201806545032). Special thanks to Dr. Jifeng Liang of Tongyu Heavy Industry Co., Ltd for providing production data.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Advanced Manufacturing TechnologyBeijing University of TechnologyBeijingChina

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