  Finding the source of a disturbance or fault in complex systems such as
industrial chemical processing plants can be a difficult task and consume a
significant number of engineering hours. In many cases, a systematic
elimination procedure is considered to be the only feasible approach but can
cause undesired process upsets. Practitioners desire robust alternative
approaches.
  This paper presents an unsupervised, data-driven method for ranking process
elements according to the magnitude and novelty of their influence. Partial
bivariate transfer entropy estimation is used to infer a weighted directed
graph of process elements. Eigenvector centrality is applied to rank network
nodes according to their overall effect. As the ranking of process elements
rely on emerging properties that depend on the aggregate of many connections,
the results are robust to errors in the estimation of individual edge
properties and the inclusion of indirect connections that do not represent the
true causal structure of the process.
  A monitoring chart of continuously calculated process element importance
scores over multiple overlapping time regions can assist with incipient fault
detection. Ranking results combined with visual inspection of information
transfer networks is also useful for root cause analysis of known faults and
disturbances. A software implementation of the proposed method is available.
