Abstract
Ensemble classifiers tend to outperform their component base classifiers when the training data are subject to variability. This intuitively makes ensemble classifiers useful for application to the problem of aircraft fault detection. Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data represent proper operation. In the domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. Instead we propose a method where the mismatch between the actual flight maneuver being performed and the maneuver predicted by a classifier is a strong indicator that a fault is present. To develop this method, we use flight data collected under a controlled test environment, subject to many sources of variability. In this paper, we experimentally demonstrate the suitability of ensembles to this problem.
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References
Robert Campbell, Amulya Garga, Kathy McClintic, Mitchell Lebold, and Carl Byington. Pattern recognition for fault classification with helicopter vibration signals. In American Helicopter Society 57th Annual Forum, 2001.
Thomas G. Dietterich. Ensemble methods in machine learning. In J. Kittler and F. Roli, editors, First International Workshop on Multiple Classifier Systems, pages 1–15. Springer Verlag, Berlin, 2000.
Thomas G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40:139–158, Aug. 2000.
Paul Hayton, Bernhard Schölkopf, Linel Tarassenko, and Paul Anusiz. Support vector novelty detection applied to jet engine vibration spectra. In Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, Advances in Neural Information Processing Systems-13, pages 946–952. Morgan Kaufmann, 2001.
Edward M. Huff, Irem Y. Tumer, Eric Barszcz, Mark Dzwonczyk, and James Mc-Names. Analysis of maneuvering effects on transmission vibration patterns in an AH-1 cobra helicopter. Journal of the American Helicopter Society, 2002.
J. Kittler. Combining classifiers: A theoretical framework. Pattern Analysis and Applications, 1:18–27, 1998.
D.A. McAdams and I.Y. Tumer. Towards failure modeling in complex dynamic systems: impact of design and manufacturing variations. In ASME Design for Manufacturing Conference, volume DETC2002/DFM-34161, September 2002.
Sunil Menon and Rida Hamza. Machine learning methods for helicopter hums. In Proceedings of the 56th Meeting of the Society for Machinery Failure Prevention Technology, pages 49–55, 2002.
I.Y. Tumer and E.M. Huff. On the effects of production and maintenance variations on machinery performance. Journal of Quality in Maintenance Engineering, 8(3):226–238, 2002.
K. Tumer and J. Ghosh. Error correlation and error reduction in ensemble classifiers. Connection Science, Special Issue on Combining Artificial Neural Networks: Ensemble Approaches, 8(3 & 4):385–404, 1996.
K. Tumer and J. Ghosh. Linear and order statistics combiners for pattern classification. In A. J. C. Sharkey, editor, Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems, pages 127–162. Springer-Verlag, London, 1999.
V. Venkatasubramanian, R. Vaidyanathan, and Y. Yamamoto. Process fault detection and diagnosis using neural networks—i. steady-state processes. Computers and Chemical Engineering, 14(7):699–712, 1990.
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Oza, N.C., Tumer, K., Tumer, I.Y., Huff, E.M. (2003). Classification of Aircraft Maneuvers for Fault Detection. In: Windeatt, T., Roli, F. (eds) Multiple Classifier Systems. MCS 2003. Lecture Notes in Computer Science, vol 2709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44938-8_38
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DOI: https://doi.org/10.1007/3-540-44938-8_38
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