Causality-driven sequence-to-sequence gated recurrent unit for performance-indicator-related root cause diagnosis

Process monitoring and root cause diagnosis (RCD) are significant for maintaining process safety and ensuring product quality in industrial processes. Existing RCD methods have achieved great success under linear and stationary assumptions, which limits their application in complex industrial processes. In addition, causality analysis of the performance indicator (PI) helps to precisely identify the path of propagation of faults and locate the root cause that leads to performance degradation. However, PI is not online measurable, which makes it difficult to achieve PI-related RCD in time. A novel causality-driven sequence-to-sequence gated recurrent unit (CSGRU) is proposed for PI-related RCD to address these issues. The proposed method is built under the distributed process monitoring framework based on a PI-related process decomposition strategy to first locate the faulty unit. CSGRU is integrated with the concept of Granger causality (GC) to learn nonlinear and dynamic causal dependencies. Sequence-to-sequence multi-task learning is introduced to avoid time-consuming pairwise causal analysis and improves the predictive accuracy even under strong sparsity constraints. The predictive contribution statistic is built to obtain the real-time faulty causal graph, which contains the causal impacts on PI without using online PI data. Finally, through a reverse causal inference from effect to cause, the fault propagation path is identified backward from PI, and the root cause is subsequently located, which leads to the degradation of PI. The effectiveness of the proposed method is validated on two benchmarks, the Tennessee Eastman process (TEP) and the vinyl acetate monomer (VAM) plant model, and a real industrial application on the three-phase flow facility (TPF).