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Journal of Applied Mathematics
Volume 2012, Article ID 809243, 17 pages
Research Article

Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set

1Department of Science, Shenyang University of Chemical Technology, Liaoning, Shenyang 110142, China
2Lab of Industrial Control Networks and Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Liaoning, Shenyang 110016, China
3College of Information Engineering, Shenyang University of Chemical Technology, Liaoning, Shenyang 110142, China

Received 2 May 2012; Revised 1 July 2012; Accepted 2 July 2012

Academic Editor: Zhiwei Gao

Copyright © 2012 Jinna Li 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.


A novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT) detection method and k-nearest neighbor (KNN) rule-based statistical process control (SPC) approach are integrated to construct a flexible and adaptive detection scheme for the control process with nonlinear, dynamic, and multimodal cases. Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. Based on it, the control limit is computed in terms of both KNN rule and SPC method, such that we can identify whether the current data is normal or not by online approach. Noted that the control limit obtained changes with updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained, which is the second merit in this paper. The efficiency of the developed method is demonstrated by the numerical examples and an industrial case.