基于分层随机梯度辨识算法的传感器故障检测方法

提出一种基于两阶段递推随机梯度参数辨识的传感器故障的在线检测方法.相对于最小二乘参数辨识算法,随机梯度参数辨识算法所需计算量更小.针对计算能力受限的系统,提出基于随机梯度参数辨识的检测算法.通过分析可知,参数辨识精度越高时检测精度越高.为提高精度,给出基于两阶段递推随机梯度参数辨识的检测算法并设计基于最新估计信息的残差.除此之外,还给出新的检测算法与原有的基于最小二乘检测算法计算量的对比分析,并通过仿真实例,证明新的检测算法的优越性及有效性.

Sensor Fault Detection Based on Hierarchical Stochastic Gradient Identification Algorithm

A fault detection method based on hierarchical stochastic gradient algorithm is presented for sensor fault. Compared with the least square identification algorithms, stochastic gradient(SG) identification algorithm has less computational load. The fault detection method is proposed based on stochastic gradient algorithm for the system with limited computing power. The analysis reveals that the higher estimation accuracy of identification algorithm, the higher estimation accuracy of detection method. To improve the accuracy, the hierarchical identification principle is used firstly, and then the unknown true parameters of residual are replaced by the latest estimation. The calculation loads analysis of the proposed algorithm is given. In addition, a simulation example is employed to show the advantage of the proposed approach in sensor fault detection.