Control Chart Pattern Recognition Method Based on Improved One-dimensional Convolutional Neural Network

• Main Authors: Xu, Jie, Lv, Huichun, Zhuang, Zilong, Lu, Zhiyao, Zou, Dewei, Qin, Wei
• Format: Conference Proceeding
• Language: English
• Subjects: Control chart; Convolutional neural network; Monte Carlo simulation; Pattern recognition; Statistical process control
• ISSN: 2405-8963; 2405-8963
• DOI: 10.1016/j.ifacol.2019.11.418

The application of statistical process control (SPC) has promoted production quality improvement of many enterprises. As a core tool of SPC, control chart is used to reflect the production state. In addition to normal pattern, abnormalities in the production process can be summarized in seven basic control chart patterns (CCPs). The recognition of CCPs is helpful to identify quality failures and find root abnormal causes in time. Convolutional neural network (CNN) is a classical model in the field of deep learning. CNN can automatically extract features from the raw data, so the operation of constructing manual features can be omitted. In this paper, the one-dimensional CNN is applied to the recognition of CCPs and achieves 98.96% average recognition accuracy in 30 tests. What’s more, even if there is a deviation between the distribution of test data and training data, the model still shows excellent generalization performance.