The causality analysis of incipient fault in industrial processes using dynamic data stream transfer entropy

The propagation of the developing incipient fault embeds potential risks to the safety management of industrial processes. The transfer entropy (TE) based causality analysis method is an effective solution for early detection and timely intervention of fault propagation. However, the slow-varying incipient fault could not be analyzed timely since the conventional TE is restricted by the limited data processing capacity and fixed data selection mode. For real-time causality analysis of developing industrial fault, we proposed the dynamic data stream transfer entropy (DDSTE) algorithm and presented the DDSTE-based causality analysis method in this paper. Firstly, the conventional static data model is replaced by dynamic data stream model, which could be updated and expanded with continues import of new data and would not be restricted by the limited storage capacity. Secondly, concerning the varying timing feature of incipient fault, the adaptive data sampling window is designed to match with incipient fault evolution stages. Thirdly, the long-lasting fault sequence is coarse-grained to improve the calculation efficiency for on-line using. Compared to conventional methods, DDSTE-based causality analysis outperforms in rapid tracking of incipient fault propagation and accurate estimation of fault location. The data collected from a real coal gasification process is used to verify the reliability and superiority of the proposed method.