Distributed process monitoring of the large-scale system using spatio-temporal-causality and Wasserstein-distance-based canonical variate analysis

Distributed process monitoring gains popularity recently to perform system health management for large-scale industrial processes and support the decision-making for system maintenance. However, process monitoring for complex large -scale systems using distributed approaches is often challenging due to significant nexus among variables. Therefore, this article proposed a novel distributed process monitoring method to achieve efficient monitoring with a reasonable and interpretable division scheme which is only given by the spatial distribution of each variable and the results of Granger causality analysis. At each subblock, a local canonical variate analysis model with Wasserstein-distance-based indices can be built to monitor each local system. With the help of a Bayesian inference strategy, all the local monitoring results are fused into a global one. Then, from both block-level and variable-level, the proposed hierarchical fault isolation method can sort out candidates for the rooting causality analysis of the detected fault, respectively. Depending on the causal analysis, the rooting cause can be identified from the intersection of two candidate sets, thereby virtualizing the propagation path of a fault. Lastly, the presented methodology of distributed process monitoring is verified by a numeral case study and the Tennessee Eastman (TE) benchmarking platform, respectively. The conclusions show that the presented methodology can perform more accurately and efficiently than traditional approaches. In particular, the proposed method can detect simulated faults in a mathematical case and the fault 15 in the TE process with nearly 100 % and 94.72 %, respectively, in terms of fault detection rates, which is barely achieved by reported methods