Recognition of Control Chart Pattern Using Improved Supervised Locally Linear Embedding and Support Vector Machine

Abstract

Control chart patterns (CCPs) have been widely utilized for machining process control, the effective recognition of abnormal CCPs can significantly narrow the set of possible assignable causes with shortening the diagnostic process to improve the intelligence of quality monitoring. This paper proposes a method for control chart classification based on improved supervised locally linear embedding (SLLE) and support vector machine (SVM). The present work extracts 12 dimensional statistical feature and shape feature of the control chart, reduces the dimensionality of high dimensional feature set by SLLE, and estimates the neighborhood size and embedding dimension with normalized cuts (Ncut) criterion. Genetic algorithm (GA) is utilized to optimize SVM classifier by searching the best values of the SVM parameters. According to the data set generated by Monte Carlo simulation, the simulation result and performance are analyzed and compared the recognition accuracy with fore-and-aft dimensionality reduction, different descending dimension algorithms and various classifiers. The result demonstrates the proposed approach can recognize CCPs effectively.