Abstract
This paper describes a new approach for the recognition of control chart patterns (CCPs). The approach uses features extracted from a CCP instead of the unprocessed CCP data or its statistical properties for the recognition task. These features represent the shape of the CCP explicitly. The approach has two main steps: (1) extraction of features and (2) recognition of patterns. A set of CCP feature extraction procedures are described in the paper. The extracted features are recognized using heuristics, induction and neural network techniques. The paper presents the results of analysing several hundred control chart patterns and gives a comparison with those reported in previous work.
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