A Hybrid Deep Neural Network for Nonlinear Causality Analysis in Complex Industrial Control System
Published in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023, 2023
Recommended citation: Tian Feng, Qiming Chen*, Yao Shi, Xun Lang, Lei Xie*, Hongye Su. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (2023). https://ieeexplore.ieee.org/abstract/document/10095701
It is important to efficiently and accurately locate the fault root cause to maintain the control performance, when the industrial control system fails. However, this task is very challenging because the industrial control system is large in scale and complex in connection. This paper proposes a novel neural causality analysis network with directed acyclic graph to locate the root cause for complex industrial systems. This network fits the temporal nonlinearity and intervariable non-linearity to mine the causal graph. The proposed method is data-driven, which acts without process knowledge. Compared with the state-of-the-art, this method can effectively output accurate root cause from nonlinear and highly coupled data. The effectiveness and advantages are demonstrated by industrial cases. Download paper here