Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2012, Special Issue (2012), Article ID 809243, 17 pages.

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

Jinna Li, Yuan Li, Haibin Yu, Yanhong Xie, and Cheng Zhang

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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.

Article information

J. Appl. Math., Volume 2012, Special Issue (2012), Article ID 809243, 17 pages.

First available in Project Euclid: 3 January 2013

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Li, Jinna; Li, Yuan; Yu, Haibin; Xie, Yanhong; Zhang, Cheng. Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set. J. Appl. Math. 2012, Special Issue (2012), Article ID 809243, 17 pages. doi:10.1155/2012/809243.

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