Fault Detection Using Neural Network in Tilt Rotor

Document Type : Research Article

Authors

School of Mechanical Engineering, Shiraz University, Shiraz, Iran

Abstract

System faults, which usually lead to changes in critical system parameters or even system dynamics, may lead to degraded performance and unsafe operating conditions. Fault detection plays an important role in ensuring system safety and reliability for unmanned aerial vehicles. Artificial neural networks have an excellent potential to detect and isolate faults in complex processes. In this paper, an observer based on an adaptive neural network is presented. In this study, the adaptive neural network is designed as an intelligent learning system to detect and isolate sensor and actuator faults in a nonlinear dynamic model of an unmanned aerial vehicle. Due to the system's nonlinearity, the neural network's weighting parameters are updated using the extended Kalman filter, which increases the convergence rate of the neural network. A set of abrupt, intermittent and incipient faults are applied to a nonlinear dynamic model of a tilt rotor to evaluate the method. Due to the high update rate of neural network weighting, the proposed method can detect abrupt, intermittent and incipient faults accurately and quickly. Numerical simulation results are also given to show the performance of the proposed method, which shows the proper performance of this design.

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References

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Amirkabir Journal of Mechanical Engineering
Volume 56, Issue 3
2024
Pages 321-344

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