Efficient control chart pattern recognition through synergistic and distributed artificial neural networks

Abstract

Accurate and fast control chart pattern recognition is essential for efficient system monitoring to maintain the production of high-quality goods. This paper addresses three major issues of control chart pattern recognition: (a) transparency, (b) accuracy and (c) fast detection of abnormal patterns. A new approach is described which uses novel shape features extracted from a control chart pattern (CCP) instead of the unprocessed CCP data or its statistical properties. These features represent the shape of the CCP explicitly. A set of algorithms is described for extraction of the shape features from a CCP. The paper discusses the use of artificial neural networks for recognition of the shape features. Synergistic, distributed and distributed synergistic neural networks are proposed for learning efficiently non-linear characteristics and overlapping ranges of values of the data set describing CCPs. The paper presents the results of analysing several hundred control chart patterns and gives a comparison with those reported in previous work.

Keywords

statistical process control control charts pattern recognition neural networks distributed multistructure neural network synergistic multistructure neural network distributed synergistic multistructure neural network

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