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Using a Computational Intelligence Hybrid Approach to Recognize the Faults of Variance Shifts for a Manufacturing Process

Yuehjen E. Shao
Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan

Abstract—Statistical Process Control (SPC) chart is effective in monitoring a process. When an SPC chart monitors a univariate process, it is not difficult to determine the assignable causes due to the fact that a univariate SPC chart only monitors a single quality characteristic. However, when a Multivariate Statistical Process Control (MSPC) chart is used to monitor a multivariate process, it is complicated to determine which quality characteristic(s) at fault. This study proposes a hybrid classification model to recognize the quality characteristic(s) at fault when the variance shifts occurred in a multivariate process. The proposed mechanism includes the hybridization of Artificial Neural Network (ANN) and analysis of variance (ANOVA). The performance of the proposed approach is evaluated by conducting a series of experiments.

Index Terms—variance shift, hybrid, multivariate statistical process control chart

Cite: Yuehjen E. Shao, "Using a Computational Intelligence Hybrid Approach to Recognize the Faults of Variance Shifts for a Manufacturing Process," Journal of Industrial and Intelligent Information, Vol. 4, No. 2, pp. 131-135, March 2016. doi: 10.18178/jiii.4.2.131-135
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