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A variable moving window approach for on-line fault diagnosis in industrial processes

Although there are many literatures on process on-line fault diagnosis, the moving window of fixed length is used in most of cases. In this study, an approach based on hidden Markov model (HMM) for on-line fault diagnosis in industrial processes is proposed. In variable moving window, the length of which is modified with time, is applied to track process dynamic variables. Before fault diagnosis, the process operating condition is monitored using principal component analysis (PCA) until abnormal operation in the process is detected. Case studies from the Tennessee Eastman plant illustrate that the proposed method is effective.