Pattern Recognition of Control Chart Based on Fuzzy c-Means Clustering Algorithm

Abstract

The pattern recognition of the control chart can distinguish the Normal factors and unnatural factors in the manufacturing process, improve the quality of products in the manufacturing process, reduce costs and improve benefits in the environment of industrial big data. Due to the fact that manufacturing process data are becoming more and more complicated under the environment of big data, the fundamental assumptions of traditional statistical process control methods are no longer invalid, which makes them unreliable to recognize the control chart patterns effectively. In this paper, the Monte Carlo method is used to generate samples. One-dimensional discrete wavelet transform is used to process the original data. The fuzzy c-means clustering algorithm is used to recognize the control chart pattern. The recognition accuracy was 99.43%, and its standard deviation was 0.0028. It shows that the method based on the control chart pattern recognition has high accuracy, good stability, and is simple and efficient compared with the existing control chart pattern recognition methods.