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Mathematical Problems in Engineering
Volume 2012, Article ID 937196, 17 pages
Research Article

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

1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
3Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Received 30 August 2012; Accepted 15 November 2012

Academic Editor: Huaguang Zhang

Copyright © 2012 Ningyun Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.