基于在线增量小波LS-SVM的垂直陀螺仪残差故障检测研究

Fault Detection Research of online incremental Wavelet LS-SVM Based on Free Gyroscope Model Residual

垂直陀螺仪是无人机重要的飞行姿态传感器,其在飞行过程中实时获取无人机的飞行姿态信息,因而其故障检测对在线性有着很高的要求;最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)相比于支持向量机的具有训练速度快、计算复杂度和需要内存少的特点,且能够扩展为自回归的形式来处理动态问题;因此文章采用基于在线增量小波LS-SVM建立无人机垂直陀螺仪动态模型,实时获得实际值与模型预测值之间的残差,并依据残差对陀螺仪进行在线故障检测;实验结果表明,该方法能够对陀螺仪实现快速精确的在线检测。

Free Gyroscope is an important flight attitude sensor, and it obtains the real--time flight attitude information which dynami cally varies in the flight, so it is exigent of online failure detection. Least Squares Support Vector Machine （LS--SVM） has the characters of fast training speed, less calculation complexity and less required memory with comparison to Support Vector Machine （SVM）, and it can ex tend auto regression format to handle the dynamic problem. In this paper, the online incremental LS--SVM is utilized to build the dynamic model of UAV free gyroscope in order to obtain the real time residual between real value and pre estimating value, and then fault detection can be done according to the residuals. The experimental results testify that this method can accomplish fast and accuracy online diagnosis.