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Finding the source of a disturbance or fault in complex systems such asindustrial chemical processing plants can be a difficult task and consume asignificant number of engineering hours. In many cases, a systematicelimination procedure is considered to be the only feasible approach but cancause undesired process upsets. Practitioners desire robust alternativeapproaches.  This paper presents an unsupervised, data-driven method for ranking processelements according to the magnitude and novelty of their influence. Partialbivariate transfer entropy estimation is used to infer a weighted directedgraph of process elements. Eigenvector centrality is applied to rank networknodes according to their overall effect. As the ranking of process elementsrely on emerging properties that depend on the aggregate of many connections,the results are robust to errors in the estimation of individual edgeproperties and the inclusion of indirect connections that do not represent thetrue causal structure of the process.  A monitoring chart of continuously calculated process element importancescores over multiple overlapping time regions can assist with incipient faultdetection. Ranking results combined with visual inspection of informationtransfer networks is also useful for root cause analysis of known faults anddisturbances. A software implementation of the proposed method is available.

Plant-wide fault and disturbance screening using combined transfer entropy and eigenvector centrality analysis