Abstract
AbstractFor many on‐line fault diagnosis schemes based on process data, a moving time window is an indispensable technique to track dynamic data. In this paper, a novel approach combining variable moving window and hidden Markov model (HMM) for on‐line identification of abnormal operating conditions is proposed. The main feature of this method is that the window length can be changed with time. Before fault diagnosis, some process measurements are used for fault alarm. As a tool for feature extraction, principal component analysis (PCA) is employed in order to reduce the large number of correlated variables. The effectiveness of the approach is illustrated by case studies from the Tennessee Eastman process.
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