In multivariate quality control, the rutificial neural networks (ANN)-based pattem recognition schemes generally performed better for monitoring bivariate process mean shifts and provided more efficient information for diagnosing the source variable(s) compared to the traditional multivariate statistical process control charring. However, these schemes revealed disadvantages in term of reference bivariate pattern in identifying the joint effect and exeess false alarms in identfyhg stable process condition. In this study, feature-based ANN scheme was investigated for recognizing bivariate correlated patterns. Featurebasal input representation was utilized into an ANN training and t e* towards strengthening discrimination capability between bivariate normal and bivariate mean shift patterns. Besides indicating an effective diagnosis capability in dealing with low correlation bivariate pattern, the proposed scheme promotes a smaller network size and better monitoring capability as compared to the raw data-based ANN scheme.

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An intelligent control chart pattern recognition system is essential for efficient monitoring and diagnosis process variation in automated manufacturing environment. Artificial neural networks (ANN) have been applied for automated recognition of control chart patterns since the last 20 years. In early study, the development of control chart patterns recognizers was mainly based on generalized-ANN model. There has been an increasing trend among researchers to move beyond generalized recognizer particularly for addressing complex recognition tasks. However, the existing works mainly focus on univariate process cases. This paper aims to investigate an effective synergistic-ANN model for online monitoring and diagnosis multivariate process patterns. The recognition performances of a generalized-ANN and the parallel distributed ANN recognizers for learning dynamic patterns of multivariate process patterns were discussed.