Analysis of Concurrent Patterns in Statistical Process Control (SPC) Charts using Neural Networks

Statistical Process Control (SPC) charts play a major role in quality control systems, and their correct interpretation leads to discovering probable irregularities and errors of the production system. In this regard, various artificial neural networks have been developed to identify mainly singular patterns of SPC charts, while having drawbacks in handling multiple concurrent patterns. In this paper, a new compound method is proposed for automatic identification of concurrent patterns of SPC charts. First, by using a wavelet transform the complex concurrent pattern is decomposed into its underlying singular patterns, and then by implementing the Principal Component Analysis (PCA) jointly with a Probabilistic Neural Network (PNN), pattern types are identified. The results obtained from simulated data exhibited a success rate of 94.83% in recognizing concurrent multiple patterns.