Abstract
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.