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The paper presents a hybrid data-driven approach of anomaly detection for UAV (Unmanned Aerial Vehicle) system. Specifically, it is focused on implementing on-line abnormal discovery to improve the operating reliability of UAV. The anomaly detection framework is based on time series segmentation, associated rules mining and associated anomaly detection. Experimental results through simulation and...
Gas turbine engine anomaly detection is a critical means to ensure the safety and economic efficiency of a flight. As gas path faults make up a sizeable proportion of all the engine faults, an engine gas path anomaly detection method was proposed in the present article. Inspired by recent progress in deep learning, we explored a method that combined deep learning with traditional anomaly detection...
There are almost no on-board intelligent anomaly detection systems in most of the existing unmanned Aerial Vehicles (UAVs), and the flight status assessment still depends on ground control station. While, this method can't meet the requirement of real-time anomaly detection for UAV autonomous and safe flight. In order to achieve real-time monitoring of UAV flight status, and improve the reliability...
The detection of gas turbine engine anomalies is of great significance to its reliable economic operation. Considering the collective anomaly data to be detected sensitively, this paper presents a symbolic approach and applies it to anomaly detection of gas turbine subsystem. The trained finite state machine evaluates the posterior probabilities of observed symbol sequence. Thus, an anomaly detection...
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