Abstract
Cause-selecting chart (CSC) is effective in monitoring and diagnosing multistage processes. It discriminates between the overall and specific qualities by establishing the relationship between input and output measurements. In practice, the model relating the input and output variables must be estimated. To this end, historical data are used, which often contain outliers. The presence of outliers has a deleterious effect on the control charting procedure. To alleviate the encountered problem, a robust monitoring approach based on Huber’s M-estimator is proposed. Subsequently, the performance of the robust and non-robust CSCs is investigated using the average run length criterion while conducting a simulation study. The results reveal that the Huber-based CSC is superior to the traditional CSC due to its prompt detection of out-of-control conditions.
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