递推批量最小二乘在直升机电动舵机故障诊断中的应用

Fault Diagnosis Method Based on Recursive Batch Least Square Filter and Its Application to Helicopter's Electric Rudders

针对批量最小二乘在线辨识Volterra级数存在计算量大,数据存储空间占用多的不足,提出了一种基于递推批量最小二乘的辨识方法.该方法通过固定观测矩阵的维数来控制数据存储空间的占用,利用递推辨识的方法避免了对矩阵直接求逆,减小了计算量.针对监测对象处于稳定工作状态时,因观测数据非常相近容易导致观测矩阵出现病态的现象,引入影响因子的概念对观测数据进行取舍,以增强辨识数值的稳定性.通过在直升机电动舵机故障诊断中的实际应用证明了该方法的有效性,为基于非线性频谱分析的在线故障诊断技术提供了一个重要途径.

To reduce the computational complexity of Volterra series on-line identification based on batch least square filter and save the data store spaces, an identification method based on recursive batch least square filter was proposed and applied to nonlinear fault diagnosis. By means of this method, the data store spaces can be saved via fixing the dimensions of observation matrix, and the computational complexity can be reduced by calculating the inverse of correlation matrix in a recursive way instead of calculating it directly. Meanwhile, in order to prevent the correlation matrix from becoming ill-conditioned, the concept of the effect factor was introduced to select the data so that the numerical stability of the identification was enhanced. The effectiveness of this identification method was illustrated by applying it to the fault diagnosis of certain type of helicopter's electric rudders.