Recognition of control chart patterns using an intelligent technique

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 for recognition of common types of CCP. The proposed method includes three main modules: the feature extraction module, the classifier module and optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module multilayer perceptron neural network and support vector machine (SVM) are investigated. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, improved bees algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy.

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► We recognize the control chart patterns with hybrid system which includes three main: feature extraction, classifier and optimization.
► Feature extraction module extracts a proper set of the shape features and statistical features.
► In the classifier module, MLPNN and SVM classifier are investigated.
► In the optimization module, improved bees algorithm is proposed for optimization of the classifier.
► Simulation results show that the proposed algorithm has very high recognition accuracy.