4/10/2024 0 Comments Uav flight control systemsA second BCM4414 steps-up the 48V to 800V for transmission across the tether. At the ground station, the BCM4414 isolates and steps-down the rectified output from a single or 3-phase AC supply to 48V. The BCM4414 provides 1.8kW of power for either step-up or step-down conversion at 97%+ efficiency. Wang, S.: Wind Turbine Gearbox Fault Diagnosis Method Research Based on Covariance Matrix Manifold.Using Vicor BCMs such as the BCM4414, a power solution can be created that enables optimized system performance. Journal of Jilin University (Information Science Edition) 39(05), 546–552 (2021) Liu, Y., Liu, F., Li, X.: Bearing fault diagnosis method based on Riemannian manifold. īoothby, W.M.: An Introduction to Differentiable Manifolds and Riemannian Geometry, Revised. Īmari, S.: Information Geometry and Its Applications. In: Noise Reduction in Speech Processing. IEEEīenesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefiicient. IEEEįreeman, P., Pandita, R., Srivastava, N., Balas, G.J.: Model-based and data-driven fault detection performance for a small UAV. Wang, B., Peng, X., Jiang, M., Liu, D.: Real-time fault detection for UAV based on model acceleration engine. Qi, J., Han, J.D.: Application of wavelets transform to fault detection in rotorcraft UAV sensor failure. Tribeni, P.B., Swagatam, D.: Multi-sensor data fusion using support vector machine for motor fault detection. Kevin, N., Greg, P.: Sensor network data fault detection with maximum a posteriori selection and bayesian modeling. In: International Conference on Information Networking (ICOIN), pp. Yuan, H., Zhao, X., Yu, L.: A distributed bayesian algorithm for data fault detection in wireless sensor networks. IEEE (2003)Īnukool, L., Mark, C., Christophe, D.: Mining anomalies using traffic feature distributions. In: Proceedings of the International Joint Conference on Neural Networks, pp. Ma, J., Perkins, S.: Time-series novelty detection using one-class support vector machines. Harbin Institute of Technology (2017)ĭavid, J.H., Barbara, S.M.: Anomaly detection in streaming environmental sensor data: a data-driven modeling approach. KeywordsĬhen, Y.: Real-Time Anomaly Detection System for Unmanned Aerial Vehicle Flight Data. Finally, in order to verify the effectiveness of the method, a semi-physical simulation platform is built, and simulation results show that the method is efficient and robust, which can quickly detect the real-time faults of the sensors in UAV flight control system, and has the potential for real flight verification. Then, the similarity degree between the real-time correlation coefficient matrix and the reference matrix is calculated in the Riemann space, and the working state of the sensor is judged by whether the similarity degree exceeds the limit value. Firstly, a correlation coefficient matrix can be calculated from the data of a sensor in flight control system to quantitatively represent the correlation characteristics of each channel data in a sensor. Aiming at the difficulty of real-time fault diagnosis of sensors in flight control system of unmanned aerial vehicle (UAV), a fault diagnosis method based on Pearson correlation coefficient and Riemannian manifold is proposed.
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