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Control chart pattern recognition using a novel hybrid intelligent method
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DOI：10.1016/j.asoc.2010.10.016.png)
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Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of common types of CCP. The proposed method includes three main modules: a feature extraction module, a classifier module and an optimization module. In the feature extraction module, the multi-resolution wavelets (MRW) are proposed as the effective features for representation of CCPs. These features are novel in this area. In the classifier module, because of the promising generalization capability of support vector machines, a multi-class SVM (SVM) based classifier is proposed. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, an efficient genetic algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results confirm that the proposed system outperforms other methods and shows high recognition accuracy about 99.37%. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Control chart pattern recognition
Multi-resolution wavelet transform
Support vector machine
Genetic algorithm
Hybrid intelligent method
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IF:
6.6
论文数:
1.4W
被引数:
4.8W
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