Online Monitoring and Fault Diagnosis of Multivariate-attribute Process Mean Using Neural Networks and Discriminant Analysis Technique

In some statistical process control applications, the process data are not Normally distributed and characterized by the combination of both variable and attributes quality characteristics. Despite different methods which are proposed separately for monitoring multivariate and multi-attribute processes, only few methods are available in the literature for monitoring multivariate-attribute processes. In this paper, we develop discriminant analysis technique for monitoring the mean vector of correlated multivariate-attribute quality characteristics in the first module. Then in the second module, a novelty approach based on the combination of artificial neural network (ANN) and discriminant analysis is proposed for detecting different mean shifts. The proposed approach is also able to diagnose quality characteristic(s) responsible for out-of-control signals after detecting different step mean shifts. A numerical example based on simulation is given to evaluate the performance of the proposed methods for detection and diagnosis purposes. The detecting performance of the second module is also compared with the extended T2 control chart and with the extension of an ANN in the literature. The results confirm that the proposed method outperforms both methods.