A fault prediction approach for process plants using fault tree analysis in sensor maiaolfunction

Zongxiao Yang; Xiaobo Yuan; Zhiqiang Feng; Kazuhiko Suzuki; Akira Inoue

doi:10.1109/ICMA.2006.257729

A fault prediction approach for process plants using fault tree analysis in sensor maiaolfunction

Zongxiao Yang, Xiaobo Yuan, Zhiqiang Feng, Kazuhiko Suzuki, Akira Inoue

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

16 Citations (Scopus)

Abstract

In this paper, a fault prediction approach for process plants using fault tree analysis is presented in the presence of no or false information of certain sensor. The fault propagation model is constructed by causal relationships from fault tree analysis (FTA). Knowledge about system failure, which is obtained from the fault propagation model, is represented as abnormality patterns in process variables and stored in the knowledge base. The prediction system can identify the cause of system malfunction considering no or false information of sensors by matching the pattern data from process plants with the abnormality pattern in the knowledge base. From unavailability of basic events and sensors, the estimated rates are provided for sequence checking in fault prediction. The proposed approach is applied successfully to a reactor control and protection system (RCPS).

Original language	English
Title of host publication	2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006
Pages	2415-2420
Number of pages	6
DOIs	https://doi.org/10.1109/ICMA.2006.257729
Publication status	Published - Dec 1 2006
Event	2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006 - Luoyang, China Duration: Jun 25 2006 → Jun 28 2006

Publication series

Name	2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006
Volume	2006

Other

Other	2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006
Country/Territory	China
City	Luoyang
Period	6/25/06 → 6/28/06

Keywords

Check sequence
Fault prediction
Fault tree analysis
Knowledge base
Sensor malfunction

ASJC Scopus subject areas

Artificial Intelligence
Software
Mechanical Engineering
Control and Systems Engineering

Access to Document

10.1109/ICMA.2006.257729

Cite this

Yang, Z., Yuan, X., Feng, Z., Suzuki, K., & Inoue, A. (2006). A fault prediction approach for process plants using fault tree analysis in sensor maiaolfunction. In 2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006 (pp. 2415-2420). [4026478] (2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006; Vol. 2006). https://doi.org/10.1109/ICMA.2006.257729

A fault prediction approach for process plants using fault tree analysis in sensor maiaolfunction. / Yang, Zongxiao; Yuan, Xiaobo; Feng, Zhiqiang et al.

2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006. 2006. p. 2415-2420 4026478 (2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yang, Z, Yuan, X, Feng, Z, Suzuki, K & Inoue, A 2006, A fault prediction approach for process plants using fault tree analysis in sensor maiaolfunction. in 2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006., 4026478, 2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006, vol. 2006, pp. 2415-2420, 2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006, Luoyang, China, 6/25/06. https://doi.org/10.1109/ICMA.2006.257729

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abstract = "In this paper, a fault prediction approach for process plants using fault tree analysis is presented in the presence of no or false information of certain sensor. The fault propagation model is constructed by causal relationships from fault tree analysis (FTA). Knowledge about system failure, which is obtained from the fault propagation model, is represented as abnormality patterns in process variables and stored in the knowledge base. The prediction system can identify the cause of system malfunction considering no or false information of sensors by matching the pattern data from process plants with the abnormality pattern in the knowledge base. From unavailability of basic events and sensors, the estimated rates are provided for sequence checking in fault prediction. The proposed approach is applied successfully to a reactor control and protection system (RCPS).",

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A fault prediction approach for process plants using fault tree analysis in sensor maiaolfunction

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Keywords

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