In large-scale industrial processes, a fault can easily propagate between process units due to the interconnections of material\r\nand information flows. Thus the problem of fault detection and isolation for these processes is more concerned about the root\r\ncause and fault propagation before applying quantitative methods in local models. Process topology and causality, as the key\r\nfeatures of the process description, need to be captured from process knowledge and process data. The modelling methods from\r\nthese two aspects are overviewed in this paper. From process knowledge, structural equation modelling, various causal graphs,\r\nrule-based models, and ontological models are summarized. From process data, cross-correlation analysis, Granger causality and\r\nits extensions, frequency domain methods, information-theoretical methods, and Bayesian nets are introduced. Based on these\r\nmodels, inference methods are discussed to find root causes and fault propagation paths under abnormal situations. Some future\r\nwork is proposed in the end.
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