Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Hotelling T2 Index Based PCA Method for Fault Detection in Transient State Processes

과도상태에서의 고장검출을 위한 Hotelling T2 Index 기반의 PCA 기법

  • Asghar, Furqan (School of Electronics and Information Engineering, Kunsan National University) ;
  • Talha, Muhammad (School of Electronics and Information Engineering, Kunsan National University) ;
  • Kim, Se-Yoon (School of Electronics and Information Engineering, Kunsan National University) ;
  • Kim, SungHo (Department of Control and Robotics Engineering, Kunsan National University)
  • ;
  • ;
  • 김세윤 (군산대학교 전자정보공학부) ;
  • 김성호 (군산대학교 제어로봇공학과)
  • Received : 2016.01.04
  • Accepted : 2016.02.17
  • Published : 2016.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

Due to the increasing interest in safety and consistent product quality over a past few decades, demand for effective quality monitoring and safe operation in the modern industry has propelled research into statistical based fault detection and diagnosis methods. This paper describes the application of Hotelling $T^2$ index based Principal Component Analysis (PCA) method for fault detection and diagnosis in industrial processes. Multivariate statistical process control techniques are now widely used for performance monitoring and fault detection. Conventional methods such as PCA are suitable only for steady state processes. These conventional projection methods causes false alarms or missing data for the systems with transient values of processes. These issues significantly compromise the reliability of the monitoring systems. In this paper, a reliable method is used to overcome false alarms occur due to varying process conditions and missing data problems in transient states. This monitoring method is implemented and validated experimentally along with matlab. Experimental results proved the credibility of this fault detection method for both the steady state and transient operations.

Keywords

References

  1. R. Isermann, Fault Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer, 2006.
  2. C. Angeli, "On-line fault detection techniques for technical systems: A survey," International Journal of Comuputer Science & Applications, vol. 1, no. 1, pp. 12-30, 2004.
  3. J. E. Jackson and G. S. Mudholkar, "Control procedures for residual associated with principal component analysis," Technometrics, vol. 21, no. 3, pp. 341-349, 1979. https://doi.org/10.1080/00401706.1979.10489779
  4. T. Kourti, "Process analysis and abnormal situation detection: From theory to practice," IEEE Control System Magazine, vol. 22, no. 5, pp. 10-25, 2002. https://doi.org/10.1109/MCS.2002.1035214
  5. B. M. Wise and N. B. Gallagher, "The process chemometrics approach to process monitoring and fault detection," Journal of Process Control, vol. 6, no. 6, pp. 329-348, Dec. 1996. https://doi.org/10.1016/0959-1524(96)00009-1
  6. J. Chen and R. J. Patton, Robust Model based Diagnosis for Dynamics Systems, Academic Publisher, 1999.
  7. H. Hotelling, Multivariate Quality Control Illustrated by the Testing of Sample Bombsights, Selected Techniques of Statistical Analysis, Mcgraw-hill, NewYork, 1947.
  8. Thamara Villegas, Maria Jesus Fuente and Miguel Rodriguez, "Principal Component Analysis for Fault Detection and Diagnosis Experience with a Pilot Plant," Advances in Computational Intelligence, Man-Machine Systems and Cybernetics, pp. 147-152, 2010.
  9. L. H. Chiang, E. L. Russell, and R. D. Braatz, Fault Detection and Diagnosis in Industrial Systems, Springer-Verlag, 2001.
  10. D. Antory, "Application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine: A case study," Mechanical Systems and Signal Processing, vol. 21, pp. 795-808, 2007. https://doi.org/10.1016/j.ymssp.2005.11.005