Control chart pattern recognition using a new type of self-organizing neural network

Abstract

Abstract

Control charts as used in statistical process control can exhibit six principal types of patterns: normal, cyclic, increasing trend, decreasing trend, upward shift and downward shift. Apart from normal patterns, all the other patterns indicate abnormalities in the process that must be corrected. Accurate and speedy detection of such patterns is important to achieving tight control of the process and ensuring good product quality. This paper describes a new type of neural network for control chart pattern recognition. The neural network is self-organizing and can learn to recognize new patterns in an on-line incremental manner. The key feature of the proposed neural network is the criterion employed to select the firing neuron, i.e. the neuron indicating the pattern class. The paper gives a comparison of the results obtained using the proposed network and those for other self-organizing networks employing a different firing criterion.