simultaneous monitoring of multivariate-attribute process mean and variability using artificial neural networks

in some statistical process control applications, the quality of the product is characterized by thecombination of both correlated variable and attributes quality characteristics. in this paper, we propose anovel control scheme based on the combination of two multi-layer perceptron neural networks forsimultaneous monitoring of mean vector as well as the covariance matrix in multivariate-attribute processeswhose quality characteristics are correlated. the proposed neural network-based methodology not onlydetects separate mean and variance shifts, but also can efficiently detect simultaneous changes in meanvector and covariance matrix of multivariate-attribute processes. the performance of the proposed neuralnetwork-based methodology in detecting separate as well as simultaneous changes in the process is evaluatedthorough a numerical example based on simulation in terms of average run length criterion and the resultsare compared with a statistical method based on the combination of two control charts that are developed formonitoring the mean vector and covariance matrix of multivariate-attribute processes, respectively. theresults of model implementation on numerical example show the superior detection performance of theproposed nn-based methodology rather than the developed combined statistical control charts.