Name
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. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/3-540-44938-8_38 | Multiple Classifier Systems |
Keywords | Field | DocType |
aircraft maintenance,training data,important problem,possible faulty operation mode,aircraft maneuvers,aircraft fault detection,flight data,automated fault detection,actual flight maneuver,proper operation,fault detection,data collection,failure analysis,data processing,vibration | Data mining,Radial basis function network,Data processing,Radial basis function,Fault coverage,Fault detection and isolation,Computer science,Simulation,Aircraft maintenance,Vibration,Classifier (linguistics),Distributed computing | Conference |
Volume | ISSN | ISBN |
2709 | 0302-9743 | 3-540-40369-8 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| nikunj c oza | 1 | 694 | 54.32 |
| kagan tumer | 2 | 1632 | 168.61 |
| Irem Y. Tumer | 3 | 31 | 8.07 |
| Edward M. Huff | 4 | 0 | 0.34 |