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http://dx.doi.org/10.18419/opus-12521
Autor(en): | Weiss, Matthias Staudacher, Stephan Becchio, Duilio Keller, Christian Mathes, Jürgen |
Titel: | Steady-state fault detection with full-flight data |
Erscheinungsdatum: | 2022 |
Dokumentart: | Zeitschriftenartikel |
Seiten: | 24 |
Erschienen in: | Machines 10 (2022), No. 140 |
URI: | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-125401 http://elib.uni-stuttgart.de/handle/11682/12540 http://dx.doi.org/10.18419/opus-12521 |
ISSN: | 2075-1702 |
Zusammenfassung: | Aircraft engine condition monitoring is a key technology for increasing safety and reducing maintenance expenses. Current engine condition monitoring approaches use a minimum of one steady-state snapshot per flight. Whilst being appropriate for trending gradual engine deterioration, snapshots result in a detrimental latency in fault detection. The increased availability of non-mandatory data acquisition hardware in modern airplanes provides so-called full-flight data sampled continuously during flight. These datasets enable the detection of engine faults within one flight by deriving a statistically relevant set of steady-state data points, thus, allowing the application of machine-learning approaches. It is shown that low-pass filtering before steady-state detection significantly increases the success rate in detecting steady-state data points. The application of Principal Component Analysis halves the number of relevant dimensions and provides a coordinate system of principal components retaining most of the variance. Consequently, clusters of data points with and without engine fault can be separated visually and numerically using a One-Class Support Vector Machine. High detection rates are demonstrated for various component faults and even for a minimum instrumentation suite using synthesized datasets derived from full-flight data of commercially operated flights. In addition to the tests conducted with synthesized data, the algorithm is verified based on operational in-flight measurements providing a proof-of-concept. Consequently, the availability of continuously sampled in-flight measurements combined with machine-learning methods allows fault detection within a single flight. |
Enthalten in den Sammlungen: | 06 Fakultät Luft- und Raumfahrttechnik und Geodäsie |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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machines-10-00140-v2.pdf | 4,21 MB | Adobe PDF | Öffnen/Anzeigen |
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