Hierarchical Causal Graph-Based Fault Root Cause Diagnosis and Propagation Path Identification for Complex Industrial Process Monitoring

Abstract

Modern complex industrial processes are increasingly integrated and automated. There are numerous control loops in the process system and the closed-loop feedback makes units strongly coupled. This makes the faults propagate and evolve with the material and energy flow networks. Thus, it poses a great challenge to the fault root cause diagnosis and propagation path identification for industrial processes. This paper proposes a fault root cause diagnosis and propagation path identification strategy for complex industrial process based on hierarchical causal graph (HCG). Firstly, the process is divided into several sub-blocks according to process knowledge. The monitoring model is built for fault detection. Secondly, the fault-related sub-blocks and variables are selected based on contribution plot, and the causal analysis based on transfer entropy is performed on them to construct HCG. In this way, the causal connections can be significantly reduced. Subsequently, a propagation path identification method based on causal feature long short-term memory (CF-LSTM) is designed. Finally, real data from hot strip mill process (HSMP) and their comparative experiments are adopted to validate the fault diagnosis performance of the proposed framework.