工业工程 ›› 2021, Vol. 24 ›› Issue (5): 108-116.doi: 10.3969/j.issn.1007-7375.2021.05.014

• 专题论述 • 上一篇    下一篇

基于模糊c均值聚类算法的控制图模式识别

张和平, 李俊武   

  1. 南昌大学 经济管理学院,江西 南昌 330031
  • 收稿日期:2020-05-03 发布日期:2021-11-02
  • 作者简介:张和平(1963—),男,福建省人,副教授,硕士,主要研究方向为质量控制、模式识别、工业工程
  • 基金资助:
    江西省研究生创新专项基金资助项目(YC2019-S075)

Recognition of Control Chart Patterns Using Fuzzy c-Means Algorithm

ZHANG Heping, LI Junwu   

  1. School of Economics and Management, Nanchang University, Nanchang 330031, China
  • Received:2020-05-03 Published:2021-11-02

摘要: 控制图模式识别能够区分制造过程中的一般因素与异常因素,提高制造过程中的产品质量,减少成本,提高效益。利用蒙特卡洛方法产生样本;采用一维离散小波变换处理原始数据;利用模糊c均值聚类算法进行控制图模式识别。识别准确率99.43%,其标准差为0.002 8。这表明基于该方法的控制图模式识别准确率高,稳定性好,较现有的控制图模式识别方法具有简易、高效等特点。

关键词: 控制图模式识别, 模糊c均值聚类算法, 小波变换

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

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