Fast and accurate recognition of control chart patterns using a time delay neural network

Abstract

Pattern recognition is a critical issue in statistical process control because unnatural patterns displayed by control charts can be associated with specific causes that adversely impact on the manufacturing process. Recently, neural networks have been widely investigated as an effective approach to control chart pattern (CCP) recognition. However, most of the research in this field has used traditional back propagation networks (BPNs) that cannot deal with patterns that vary over time in an online CCP recognition scheme. This causes a pattern misclassification problem in nearly all neural network-based studies in the field of online CCP recognition. The present article presents a novel application of utilizing a time delay neural network (TDNN)-based model to address this problem. The TDNN, with its special architecture, can represent relationships between patterns in a time sequence, and is, therefore, suitable to be trained with dynamic patterns that change over time. Numerical results indicate that the pattern misclassification problem has been addressed effectively by the proposed TDNN-based model. When compared with traditional BPNs, the TDNN model has better performance in both recognition accuracy and speed. In comparison with traditional control chart approaches, the proposed model is capable of superior performance of average run length, while the category of the unnatural CCP can also be accurately identified.