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

## TL;DR

This paper introduces an unsupervised, data-driven approach combining transfer entropy and eigenvector centrality to identify and rank process elements responsible for faults in complex industrial systems, aiding in fault detection and root cause analysis.

## Contribution

It presents a novel method that uses transfer entropy and eigenvector centrality for robust, unsupervised fault source identification in process networks, with a software implementation provided.

## Key findings

- Effective ranking of process elements based on influence.
- Robustness to estimation errors and indirect connections.
- Useful for fault detection and root cause analysis.

## Abstract

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.