Gripen E, a fighter jet developed by Saab, has to fulfill a number of specifications and is therefore tested thoroughly. This project is about detecting anomalies in such tests and thereby improving the automation of the test data evaluation. The methodology during this project was to model the expected deviation between the measured signals and the corresponding signals from a fuel system model using machine learning methods. This methodology was applied to the mass in one of the fuel tanks. The challenge lies in the fact that the expected deviation is unknown and dependent on the operating conditions of the fuel system in the aircraft. Furthermore, two different machine learning approaches to estimate a prediction interval, within which the residual was expected to be, were tested. These were quantile regression and a variance estimation based method. The machine learning models used in this project were LSTM, Ridge Regression, Random Forest Regressor and Gradient Boosting Regressor. One of the problems encountered was imbalanced data, since different operating modes were not equally represented. Also, whether the time dependency of the signals had to be taken into account was investigated. Moreover, choosing which input signals to use for the machine learning methods had a large impact on the result. The concept appears to work well. Known anomalies were detected, and with a low degree of false alarms. The variance estimation based approach seems superior to quantile regression. For data containing anomalies, the target signal drifted away significantly outside the boundaries of the prediction interval. Such test flights were flagged for anomaly. Furthermore, the concept was also successfully verified for another fuel tank, with only minor and obvious adaptations, such as replacing the target signal with the new one.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-414208 |
| Date | January 2020 |
| Creators | Tysk, Carl, Sundell, Jonathan |
| Publisher | Uppsala universitet, Signaler och system, Uppsala universitet, Signaler och system |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC F, 1401-5757 ; 20030 |
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