Abstract
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.
Highlights
The rotor unmanned aerial vehicle (UAV) [1] is an aircraft that does not carry a pilot
For rotor UAV flight system sensors, the fault diagnosis method [4] is mostly model based [5], which [6] relies on the accurate model of the system [7]
In view of the above discussion, the fault diagnosis of rotor UAV flight control system sensor has been taken as an example and a fault diagnosis method for deep belief network (DBN) is presented by this paper
Summary
The rotor UAV [1] is an aircraft that does not carry a pilot. It has been widely used in military and civilian fields for its unique advantages, so it is indispensable to ensure the safety and reliability of the rotor UAV flight control system. For rotor UAV flight system sensors, the fault diagnosis method [4] is mostly model based [5], which [6] relies on the accurate model of the system [7]. In reference [14], a novel data-driven adaptive neuron fuzzy inference system (ANFIS)-based approach was proposed to detect on-board navigation sensor faults in UAVs. The main advantages of this algorithm are that it allows the Kalman filter to estimate real-time model-free residual and ANFIS to build a reliable fault detection system. In view of the above discussion, the fault diagnosis of rotor UAV flight control system sensor has been taken as an example and a fault diagnosis method for DBN is presented by this paper. By training a large number of offline historical sample data, the optimal network parameters obtained perform the feature extraction of fault and analyze more essential data features to make it easy to detect faults
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
