Control charts are useful tool in detecting out-of-control situations in process data. There are many unnatural patterns that may exist in process data indicating the process is out of control. The presence of unnatural patterns implies that a process is affected by assignable causes, and corrective actions should be taken. Identification of unnatural patterns can greatly narrow the set of possible causes that must be investigated, and thus the diagnostic work could be reduced in length. This paper presents a modified self-organizing neural network developed for control chart pattern analysis. The aim is to develop a pattern clustering approach when no prior knowledge of the unnatural patterns is available. This paper also investigates the use of features extracted from wavelet analysis as the components of the input vectors. Experimental results and comparisons based on simulated and real data show that the proposed approach performs better than traditional approach. Our research concluded that the extracted features can improve the performance of the proposed neural network.
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