Deep Belief Net-Based Fault Diagnosis of Flight Control System Sensors

The present study implements a model based on the deep belief net (DBN) into the sensor fault diagnosis of the flight control system. The principle of DBN system identification was adopted to simulate and establish a nonlinear observer of the unmanned aerial vehicle (UAV) for the online estimation o...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-09, Vol.1631 (1), p.12186
Main Authors: Guo, Yingnan, Ning, Wenhui, Wan, Fangyi
Format: Article
Language:English
Subjects:
Digital simulation
Fault detection
Fault diagnosis
Flight control systems
Flight training
Physics
Sensors
Signal reconstruction
System identification
Unmanned aerial vehicles
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Summary:The present study implements a model based on the deep belief net (DBN) into the sensor fault diagnosis of the flight control system. The principle of DBN system identification was adopted to simulate and establish a nonlinear observer of the unmanned aerial vehicle (UAV) for the online estimation of sensors' output, which identified the fault type by analyzing the residuals of estimated data and the actual output. After the fault detection is completed, the measured value of the faulty sensor is isolated and replaced by the observer-generated value, in order to ensure the UAV normal flight. The proposed DBN model uses the data of normal flight control system sensors as training samples for offline training. A flight control digital simulation system was established to select the optimal DBN model via comparative test runs. Eventually, the common faults of sensors were analyzed and diagnosed online. The results obtained strongly indicate that the proposed method ensures a rapid and accurate diagnostics and isolation of faults, as well as provides a proper signal reconstruction.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1631/1/012186