Diagnosing the source(s) of a monotonic change in the process mean vector

Abstract

When a control chart detects any departure of an underlying process from in-control state, a root cause analysis should be initiated by process engineer to identify and eliminate the source(s) of contributing factors. In a multivariate environment, such information is crucial and plays an essential role for an effective root cause analysis. Different schemes have been proposed in the literature which could be used for this purpose when change type is known a priori. However, in practice, when the change type deviates from the assumed change type, the performance of these schemes deteriorates considerably. In this paper, a model based on artificial neural network is introduced which helps to diagnose the source(s) of assignable cause without requiring the exact knowledge of the change type in the mean vector of the process. A case study in chemical industry is considered to evaluate the performance of the proposed schemes when process experiences different change types. Also, the capabilities of the proposed scheme are compared to the two existing models. The correct classification percentage is used as the measure of the performance evaluation. Several other numerical examples are also used to evaluate the robustness of the model.