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Comparing residual evaluation methods for leak detection on an aircraft fuel system test-rig

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Języki publikacji
EN
Abstrakty
EN
Many authors have used a Kalman Filter (KF) or a bank of several KF's as the main component of a fault detection algorithm (see, e.g., [5, 9]). Usually, the residual (or error) from the KF is evaluated against a predetermined threshold and crossing of the threshold level triggers a fault flag. The exact nature of the residual evaluation varies from analysis of the raw signal, to application of relatively complex statistical tests [2, 9, 11]. However, it is not clear from the literature which of the many methods available offer the best results. The paper examines the application of several statistical tests to residuals of a KF implemented as part of a fault detection scheme on an aircraft fuel system simulator test-rig. The experimental results will be evaluated and discussed and recommendations will be made on which methods offer the greatest utility for rapid detection of a leak fault applied to a tank containing fluid on the test-rig. The statistical methods evaluated are: mean deviation, mean absolute deviation, mean square error, root mean square error, sum of square error, weighted sum of square error, paired-t test, r-square and chi-square mean.
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Rocznik
Strony
63--74
Opis fizyczny
Bibliogr. 11 poz., wykr.
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autor
autor
autor
Bibliografia
  • [1] Bennett P.J., Pearson J.T., Martin A., Dixon R., Application of diagnostic techniques to an experimental aircraft fuel rig, Proc. 6th IFAC Symp. Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS) 2006, Beijing, China, September 2006.
  • [2] Candy J.V., Mcclay W., Awwal A., Ferguson W., Optimal Centroid Position Estimation, SPIE 49th Annual Meeting 2004, Denver, CO, United States, 2004.
  • [3] Chen J., Patton R., Robust Model-Based Fault Diagnosis for Dynamic Systems, Kluwer Acedemic Publishers, Massachusetts, United States, 1999.
  • [4] Haber R., Alique A., Intelligent process supervision for predicting tool wear in machining processes. Journal of Mechatronics, Vol. 13, No. 8-9, 2003, pp. 825-849.
  • [5] Frank P.M., Ding X., Survey of robust residual generation and evaluation methods in observer-based fault detection systems, Journal of Process Control, Vol. 7, No. 6, 1997, pp. 403-424.
  • [6] Frank P.M., Ding X., Frequency domain approach to optimally robust residual generation and evaluation for model-based fault deletion, Automatica, Vol. 30, No. 5, 1994, pp. 789-804.
  • [7] Friedland B., Control Systems Design, McGraw-Hill, Singapore, 1987.
  • [8] Grainger R.W., Hoist J., Isaksson A.J., Ninness B.M., A parametric statistical approach to FDI for the industrial actuator benchmark. Control Engineering Practice, Vol. 3, No. 12, 1995, pp. 1757-1762.
  • [9] Kobayashi Takahisa, Simon D.L., Application of a bank of Kalman filters for aircraft engine fault diagnostics, Turbo Expo 2003, Atlanta, Georgia, June 2003.
  • [10] Piatyszek E., Voignier P., Graillot D., Fault detection on a sewer network by combination of a Kalman filter and a binary sequential probability ration test, Journal of Hydrology, No. 230, 2000, pp. 258-268.
  • [11] Sohlberg B., Monitoring and failure diagnosis of a steel strip process, IEEE Trans. Control Systems Technology , Vol. 6, No. 2, March 1998, pp. 294-303.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0033-0064
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