Nikunj C. Oza's Publications

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Classification of Aircraft Maneuvers for Fault Detection

Classification of Aircraft Maneuvers for Fault Detection. Nikunj C. Oza, Irem Y. Tumer, Kagan Tumer, and Edward Huff. In Proceedings of the Fourth International Workshop on Multiple Classifier Systems, pp. 375–384, Springer=Verlag, Surrey, UK, June 2003.

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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.

BibTeX Entry

@inproceedings{oztu03,
        author={Nikunj C. Oza and Irem Y. Tumer and Kagan Tumer and Edward Huff},
        title={Classification of Aircraft Maneuvers for Fault Detection},
        booktitle = {Proceedings of the Fourth International Workshop on
                Multiple Classifier Systems},
        publisher = {Springer=Verlag},
        address={Surrey, UK},
        editor = {Kittler, J. and Roli, F.},
        pages = {375-384},
	month = {June},
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.},
bib2html_pubtype = {Refereed Conference},
bib2html_rescat = {Ensemble Learning},
        year = {2003}
}

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