Recognition of Control Chart Patterns Using Fuzzy c-Means Algorithm

doi:10.3969/j.issn.1007-7375.2021.05.014

Abstract

Abstract: Normal factors and unnatural factors in the manufacturing process can be detected by recognition of control chart pattern (CCPR), so CCPR can improve the product quality in the manufacturing process and reduce costs and improve benefits. The Monte Carlo method is used to generate samples. The one-dimensional discrete wavelet transform is used to process the raw data, and finally fuzzy c-means (FCM) clustering algorithm is used for pattern recognition of control chart, which is simpler and more efficient than the existing control chart pattern recognition method. By continuous experiments and improvements, the recognition accuracy of 99.31% is finally achieved, with a standard deviation of 0.0028, indicating that the control chart pattern recognition method adopted in this study has high accuracy and good stability.

Key words: recognition of control chart pattern, fuzzy c-means (FCM) clustering algorithm, wavelet transform

CLC Number:

C812

ZHANG Heping, LI Junwu. Recognition of Control Chart Patterns Using Fuzzy c-Means Algorithm[J]. Industrial Engineering Journal, 2021, 24(5): 108-116.

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