Abstract
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. In this paper, we have constructed the recognition model for control chart pattern using one-dimensional discrete wavelet transform and BP neural network. Then, using Monte-Carlo method to generate the data, we have compared between the performances of the model using raw data and the model based on wavelet transform. In order to select the optimal wavelet, we have compared the recognition accuracy of different wavelets (DbN, SymN and CoifN) on different levels. The simulation results show that model based on wavelet transform in this paper have higher recognition accuracy and the recognition accuracy using the wavelet of Coif4 on the level 3 is 97.64% and relatively stable for various patterns.
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