Control chart pattern recognition using neural networks and efficient features: a comparative study

Abstract

Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. This paper investigates the design of an efficient system for recognition of the control chart patterns. This system includes two main modules: a feature extraction module and a classification module. The feature extraction module extracts a combination set of the shape features and statistical features. In the classifier module, several multilayer perceptron neural networks with different number of layers and training algorithms are investigated. The performances of the networks for speed of convergence and accuracy classification are evaluated for six classes of the CCPs. Among the different training algorithms, the resilient back-propagation (RP) algorithm represented the best convergence rate and the Levenberg---Marquardt (LM) algorithm achieved the best overall detection accuracy.