Reliable monitoring of modal parameters during a flight vibration test using autonomous modal analysis and a Kalman filter

Kurzfassung

Lightweight construction is required for efficient aircraft design, but it poses challenges due to loads and vibration during flight. Aircraft dynamic aeroelastic behavior, described by modal parameters like eigenfrequencies and damping ratios, varies with altitude and velocity, potentially leading to flutter - an unstable self-excited vibration. Predicting and preventing flutter involves identifying modal parameters through ground and flight vibration tests. However, uncertainties in flight tests, especially concerning damping ratio estimates, result from conditions regarding time varying systems, low signal-to-noise ratios, and unobservable or unmeasured influences. This study proposes a real-time uncertainty estimation method using clustering-based automated modal analysis and uncertainty reduction using a Kalman filter. For the first time, the Kalman filter-based monitoring has been applied in-flight during a flight vibration test (FVT). The German Aerospace Center (DLR) operates the ISTAR research aircraft (In-flight Systems and Technology Airborne Research), a modified Dassault Falcon 2000 LX. In 2023, an FVT with ISTAR was conducted in Germany. ISTAR, which is permanently equipped with 62 accelerometers and a certified measurement system, records vibration data and operational parameters during every flight. The study shows some of the flight data collected by this extensively instrumented aircraft. The system continuously processes online streamed data every two seconds during the FVT, conducting spectral analysis, modal parameter identification, automated modal analysis (AMA), and Kalman filtering for robust flutter monitoring. The flight test campaign included varying flight levels, fast and slow accelerations and decelerations with and without artificial excitation, enabling a comprehensive comparison of output-only modal parameter identification methods. Monitoring based on the Kalman filter was able to track eigenfrequencies and damping ratios of the aircraft robustly and continuously. These results are promising for online assessment and reliable online prediction of flutter critical speeds; however, the fight tests were performed with the aircraft in a baseline configuration, which is flutter stable in the whole flight test envelope.