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Open AccessArticle

Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

1
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2
O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN 47405, USA
3
Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1350; https://doi.org/10.3390/electronics8111350
Received: 14 October 2019 / Revised: 4 November 2019 / Accepted: 11 November 2019 / Published: 14 November 2019
(This article belongs to the Special Issue Data Analysis in Intelligent Communication Systems)
The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper. View Full-Text
Keywords: rotor UAV; data-driven; on-line; early warning; comprehensive fault diagnosis; DBN rotor UAV; data-driven; on-line; early warning; comprehensive fault diagnosis; DBN
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Chen, X.-M.; Wu, C.-X.; Wu, Y.; Xiong, N.-X.; Han, R.; Ju, B.-B.; Zhang, S. Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN. Electronics 2019, 8, 1350.

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