On-line control chart pattern detection and discrimination—a neural network approach

Abstract

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. A common difficulty in existing control chart pattern recognition approaches is that of discrimination between different types of patterns which share similar features. This paper proposes an artificial neural network based model, which employs a pattern discrimination algorithm to recognise unnatural control chart patterns. The pattern discrimination algorithm is based on several special-purpose networks trained for specific recognition tasks. The performance of the proposed model was evaluated by simulation using two criteria: the percentage of correctly recognised patterns and the average run length (ARL). Numerical results show that the false recognition problem has been effectively addressed. In comparison with previous control chart approaches, the proposed model is capable of superior ARL performance while the type of the unnatural pattern can also be accurately identified.