An improved scheme for online recognition of control chart patterns

Abstract

This paper proposes two alternative schemes for the online recognition of control chart patterns (CCPs), namely: 1) a scheme based on direct continuous recognition; 2) a scheme based on 'recognition only when necessary'. The study focuses on recognition of six CCPs plotted on the Shewhart X-bar chart, namely, random, shift-up, shift down, trend-up, trend-down and cyclic. The artificial neural network (ANN) recogniser used was based on multilayer perceptrons (MLPs) architecture. The performance of the schemes was evaluated based on percentage correct recognition, average run lengths (ARL) and average recognition attempts (ARA). The findings suggest that the online recognition should be made only when necessary. Continuous recognition is not only wasteful, but also results in poorer results. The methodology proposed in this study is a step forward in realising a truly automated and intelligent online statistical process control chart pattern recognition system.

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