A Causality Capturing Method for Diagnosis Based on Transfer Entropy by Analyzing Trends of Time Series

摘要

Since modern industrial processes become much larger and more complex, efficient and effective causality detection methods are needed to capture the process topology, diagnose root causes of widespread or even plant-wide process malfunction, and further ensure the safety of processes. A modified transfer entropy method, named trend transfer entropy, is proposed in this paper, which focuses on analyzing trends of time series rather than the original series themselves and thus, compared to the traditional transfer entropy, proves to be more robust in conditions of data drifting and noise disturbance. Moreover, the new method can reduce computational load effectively, saving valuable time before the occurrence of an accident. Simulation studies are presented to illustrate the procedure and features of the proposed method.