A Fault Prognosis Strategy Based on Time-Delayed Digraph Model and Principal Component Analysis

Abstract EN

Because of the interlinking of process equipments in process industry, event information may propagate through the plant and affect a lot of downstream process variables.

Specifying the causality and estimating the time delays among process variables are critically important for data-driven fault prognosis.

They are not only helpful to find the root cause when a plant-wide disturbance occurs, but to reveal the evolution of an abnormal event propagating through the plant.

This paper concerns with the information flow directionality and time-delay estimation problems in process industry and presents an information synchronization technique to assist fault prognosis.

Time-delayed mutual information (TDMI) is used for both causality analysis and time-delay estimation.

To represent causality structure of high-dimensional process variables, a time-delayed signed digraph (TD-SDG) model is developed.

Then, a general fault prognosis strategy is developed based on the TD-SDG model and principle component analysis (PCA).

The proposed method is applied to an air separation unit and has achieved satisfying results in predicting the frequently occurred “nitrogen-block” fault.