An adaptive DISSIM algorithm for statistical process monitoring

Recently, a novel multivariate statistical process monitoring method, known as dissimilarity algorithm(DISSIM), has been developed based on the idea that a change of operating condition can be detected by monitoring a distribution of process data set, where a dissimilarity index is introduced to quantitatively evaluate the difference between distributions of process data. However, as a fixed-model monitoring technique, it inevitably gives false alarms when applied to real processes involving slow changes. In this paper, an adaptive DISSIM(ADISSIM) algorithm is proposed for on-line updating to consecutively adapt to process slow-varying behaviors. The key to the proposed method is that whenever the old model is judged to be inefficient to capture the current normal operation status, a new monitoring model is developed by integrating the old model and the new updating data. The effectiveness of ADISSIM algorithm is successfully illustrated when applied to simulated data collected from a simple numerical process. The results clearly show that the proposed adaptive method is effective to accommodate the normal gradual changes and distinguish them from real process faults, thus providing a new feasible statistical monitoring method for the prevalent slow-varying processes. 2 2×