A Bayesian Approach for Disturbance Detection and Classification and Its Application to State Estimation in Run-to-Run Control

With growing demand for effective management of abnormal situations in process industry, disturbance detection and classification has drawn considerable interest from researchers in both industry and academia. In this paper, a disturbance detection and classification method is developed using Bayesian statistics. The theoretical derivation of the proposed method as well as its practical implementation are provided. With the introduction of preand post-change windows, detection and classification are achieved simultaneously in the proposed method through matching the posterior probability pattern to predefined patterns. An overlapping window mechanism is incorporated into the proposed method to minimize detection and classification delay. A simulation example is given to illustrate the robustness and effectiveness of the proposed disturbance detection method. One application of the proposed Bayesian disturbance detection and classification algorithm is a Bayesian enhanced exponentially weighted moving average (B-EWMA) state estimator which improves state estimation in the run-to-run control of semiconductor manufacturing processes. The superior performance of B-EWMA compared to the conventional EWMA is demonstrated using an industrial example