Parameter-Insensitive Technique for Aircraft Sensor Fault Analysis
Parameter-Insensitive Technique for Aircraft Sensor Fault Analysis
复制标题
飞机传感器故障分析的参数不敏感技术
DOI：
10.2514/3.20226
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发表时间：
1987
影响因子：
2.6
通讯作者：
J. S. Winter
中科院分区：
文献类型：
--
作者：
R. Patton;S. Willcox;J. S. Winter
本文提出了一种分析n阶系统m个测量故障的新方法。该方法利用一组观测器中每个观测器的估计误差空间来检测和隔离传感器故障。该设计被应用到一个非线性模型的无人驾驶飞机，已在以前的出版物中描述。证明了飞机传感器系统的可重构性，结果表明可以从故障传感器中快速恢复。观测误差的使用消除了状态空间计算的需要，从而产生一个有效的实时故障监控器的电传操纵飞机。本文对两种仪器故障诊断技术进行了比较.这项工作是Patton和威尔考克斯使用分析冗余来设计观测误差空间的m个分量之间的匹配而不是直接使用状态估计的想法的扩展，如Clark，3'4 Clark和Setzer，5 Frank和Keller所讨论的，6和Watanabe和Himmelblau。7动态系统中的IFD最近受到了大量的关注。讨论了分析冗余方法，而不是使用冗余硬件。分析冗余提供了来自过程的不同测量的冗余（估计）信息，通常使用观测器或卡尔曼滤波器方案。通常讨论的IFD的状态估计解决方案是基于从测量的子集生成部分或全部系统状态向量的估计的原理，当与来自其他观测器的类似估计相比时，其可以用于监视仪器的健康。IFD的状态估计解决方案的问题出现的观察员需要一个良好的线性模型的过程中，它也必须假设，系统上的干扰是很好的建模，否则有一个微不足道的影响，工厂参数的变化。这些限制导致状态估计方法对于许多真实的工程应用是不适当的。对输入引起的参数变化的敏感性导致冗余状态估计向量之间的不确定误差，并且在IFD方案中，这些误差可能导致仪器故障的错误信号。显然，在IFD系统设计之前，应该估计不确定信号的带宽。以这种方式使用频域灵敏度信息使得能够进行观测器设计的鲁棒方法。所使用的推测是，“新息”或预测误差信号包含关于被识别和控制的过程的参数变化的所有信息。因此，注意力转向使用创新为基础的方法来系统故障诊断，具有广泛的潜在应用。通过使用测量估计误差的加权作为奇偶校验，
A new method of analyzing faults in the m measurements of an nth-order system is presented. The proposed approach uses the estimation error space of each observer in a bank of observers to detect and isolate sensor faults. The designs are applied to a nonlinear model of an unmanned aircraft that has been described in previous publications. The reconfigurability of the aircraft sensor system is demonstrated, and the results show rapid recovery from a faulty sensor. The use of the observation error eliminates the need for state-space computations, thus producing an effective real-time fault monitor for fly-by-wire aircraft. N an earlier paper,1 a comparison of two techniques of instrument fault diagnosis (IFD) was made. This work is an extension of Patton and Willcox's idea of using analytical redundancy to design a match between m components of the observation error space instead of using state estimates di- rectly as discussed by Clark,3'4 Clark and Setzer,5 Frank and Keller,6 and Watanabe and Himmelblau.7 IFD in dynamic systems has received a significant amount of attention recently.2"11 Most methods described in the liter- ature discuss the analytical redundancy approach in prefer- ence to the use of redundant hardware. Analytical redundancy provides redundant (estimate) information from different measurements of a process, usually with observer or Kalman filter schemes. The commonly discussed state estimate solu- tion to IFD is based on the principle of generating estimates of part or all of the system state vector from subsets of the measurements, which when compared with similar estimates from other observers can be used to monitor the health of an instrument. The problem with the state estimate solution to IFD arises as the observer requires a good linear model of the process, and it must also be assumed that the disturbances on the system are well modeled or else have an insignificant effect on plant parameter variations. These limitations cause the state estimate approach to be inadequate for many real en- gineering applications. Sensitivity to input-induced parameter variations causes uncertain errors between redundant state estimate vectors, and in an IFD scheme these errors could cause false signaling of an instrument fault. It becomes clear that the bandwidth of uncertain signals should be estimated prior to the IFD system design. The use of frequency domain sensitivity information in this way enables a robust approach to the observer design to be made. The conjecture used is that the "innovations" or prediction error signals contain all the information concerning the parameter variations of the pro- cess being identified and controlled. Attention is thus turned toward the use of an innovations-based approach to system fault diagnosis that has wide potential applications. By using a weighting of the measurement estimation error as a parity