Model-based causal reasoning for process supervision
Automatica (Journal of IFAC)
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Data Modeling Theory and Practice
Data Modeling Theory and Practice
Hierarchical representation of complex systems for supporting human decision making
Advanced Engineering Informatics
Diagnosis of continuous valued systems in transient operating regions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Dynamic causal model diagnostic reasoning for online technical process supervision
Automatica (Journal of IFAC)
Brief Causal fault detection and isolation based on a set-membership approach
Automatica (Journal of IFAC)
Root cause detection in a service-oriented architecture
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
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In large-scale industrial processes, a fault can easily propagate between process units due to the interconnections of material and information flows. Thus the problem of fault detection and isolation for these processes is more concerned about the root cause and fault propagation before applying quantitative methods in local models. Process topology and causality, as the key features of the process description, need to be captured from process knowledge and process data. The modelling methods from these two aspects are overviewed in this paper. From process knowledge, structural equation modelling, various causal graphs, rule-based models, and ontological models are summarized. From process data, cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian nets are introduced. Based on these models, inference methods are discussed to find root causes and fault propagation paths under abnormal situations. Some future work is proposed in the end.