科研之友
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摘要
Historical normal and abnormal data sets are prerequisites for process monitoring, alarm system rationalization, fault detection, and diagnosis. This paper proposes a new method to automatically find normal and abnormal data segments from historical data sets based on variational directions of multiple process variables. The minimum time duration and the minimum amplitude shift are introduced as empirical knowledge to define underlying stages in the data sets. Two major challenges in identifying these stages are addressed by using a density-based clustering algorithm. The effectiveness of the proposed method is illustrated using numerical and industrial examples.
