A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes

Cang Wu, Huijuan Hou, Chunli Lei, Pan Zhang and Yongjun Du ()
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Cang Wu: School of Mechanical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Huijuan Hou: School of Mechanical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Chunli Lei: School of Mechanical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Pan Zhang: China School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Yongjun Du: School of Mechanical Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Mathematics, 2023, vol. 11, issue 16, 1-16

Abstract: Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot always be satisfied under continuous manufacturing processes. Recent research has considered the existence of autocorrelation, but the recognition rate is overestimated. In this paper, a novel scheme is proposed to recognize control chart patterns (CCPs) in which the inherent noise is autocorrelated. By assuming that the inherent noise follows a first-order autoregressive (AR (1)) process, the one-dimensional convolutional neural network (1DCNN) is applied for extracting features in the proposed scheme, while the grey-wolf-optimizer-based support vector machine (GWOSVM) is employed as a classifier. The simulation results reveal that the proposed scheme can effectively identify seven types of CCPs. The overall accuracy is 89.02% for all the autoregressive coefficients, and the highest accuracy is 99.43% when the autoregressive coefficient is on the interval (−0.3, 0]. Comparative experiments indicate that the proposed scheme has great potential to identify CCPs in autocorrelated processes.

Keywords: control chart patterns; autocorrelated processes; one-dimensional convolutional neural network; support vector machine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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