多传感器斜置系统故障检测的奇偶向量补偿方法

Parity Vector Compensation for Multi-Sensor Fault Detection

提出采用神经网络对多传感器斜置组件的奇偶向量进行补偿 ,从而消除安装误差、刻度系数误差以及常值偏差对奇偶向量的影响 ,提高系统对小幅值故障检测与隔离的准确性 .基于神经网络的方法与卡尔曼滤波、偏差分离估计方法相比 ,不需要各项误差的动态模型和噪声统计特性 .神经网络采用有一定时间延迟的样本进行在线学习时 ,利用补偿后的奇偶向量能够检测出有一定斜率的斜坡型故障 .

A scheme based on neural network is presented to improve the fault detection and isolation (FDI) performance of a redundant strapdown inertial measurement unit. The neural network is trained to eliminate the effects of input axis misalignment, scale factor error and biases on parity vector. One advantage of the proposed technique over other compensation algorithm is that it does not require dynamic equation of error states and statistics of noise. When the neural network is trained on line with delayed samples, the compensated decision function is sensitive to slow-varying type fault. The simulation results show that the technique could significantly improve the FDI performance of the skewed inertial sensor sets.