Authors: Karim Atashgar R Noorossana
Publish Date: 2011/10/05
Volume: 60, Issue: 9-12, Pages: 1175-1183
Abstract
When a control chart detects any departure of an underlying process from incontrol state a root cause analysis should be initiated by process engineer to identify and eliminate the sources 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 sources 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
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