Fault detection using neural network for tilting rotor

Document Type : Research Article

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School of Mechanical Engineering, Shiraz University, Shiraz, Iran

Abstract

System faults, usually lead to changes in critical system parameters or even system dynamics, may lead to reduced 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 a good potential to detect and isolate errors in complex processes. In this paper, an observer based on 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 error in a nonlinear dynamic model of an unmanned aerial vehicle. Due to the nonlinearity of the system, the weighting parameters of the neural network are updated using the extended Kalman filter, which increases the convergence rate of the neural network. A set of sudden and intermittent faults is applied to a nonlinear dynamic model of a tilting multirotor to evaluate the method. Due to the high rate of updating the neural network weightings, the proposed method is able to detect sudden and intermittent faults with appropriate accuracy and speed. 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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