2023/01/01 by Yimeng He, Le Yao, Zhiqiang Ge +1 · 31 citations
Engineering · #Advanced Data Processing Techniques #Algorithm #Artificial intelligence #Computer science #Data mining #Engineering #Fault (geology) #Fault Detection and Control Systems #Fault detection and isolation #Graph #Mineral Processing and Grinding #Process (computing) #Reliability engineering #Root cause #Root cause analysis #Theoretical computer science #Tracing
paper · doi:10.1109/tim.2023.3273686
published in IEEE Transactions on Instrumentation and Measurement 72, 1-11 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2023/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26
Fault tracing technology, including root-cause diagnosis and propagation analysis, has become a growing hot spot in the field of industrial process monitoring. However, it is currently limited by the use of restricted alarm sequence data and the analysis without fault propagation analysis. To solve these problems, this article proposes a novel fault tracing method, namely causal topology-based variable-wise generative model (CTVGM). The CTVGM is first established according to the topological order of the variable causal graph. It contains a series of causal functions that are trained with normal data. Then, fault samples can be restored by the CTVGM to build up a diagnosis index called the recovery ratio (RR), which is used to determine the root causes. Meanwhile, the fault propagation paths are inferred by the recovery routes. In addition, a hierarchical CTVGM-based fault tracing strategy is designed to reduce the computation burden and enhance the modeling efficiency for large-scale complicated processes. The effectiveness of the proposed fault tracing method is verified on a numerical example and the Tennessee Eastman process case. Compared with existing methods, the results show that the proposed method not only achieves more accurate root-cause diagnosis performance but also obtains fault tracing results that are highly consistent with the process mechanisms.