Fault detection and diagnosis for general stochastic systems using B-spline expansions and nonlinear filters
Fault detection and diagnosis for general stochastic systems using B-spline expansions and nonlinear filters
复制标题
DOI：
10.1109/tcsi.2005.851686
复制
发表时间：
2005-08
期刊：
影响因子：
--
通讯作者：
Lei Guo;Hong Wang
中科院分区：
文献类型：
--
作者：
Lei Guo;Hong Wang
本文提出了一种适用于一般随机系统的新型故障检测与诊断（FDD）算法。与经典的FDD设计不同，这里假定测量的是系统输出的分布，而非输出信号本身。这种FDD算法设计的任务是利用测量到的输出概率密度函数（PDF）以及系统的输入来构建一个基于稳定滤波器的残差生成器，以便能够检测和诊断故障。为此，采用平方根B样条展开来对输出PDF进行建模，并将相关问题转化为一个受非线性权重动态系统约束的非线性FDD算法设计问题。提出了一种基于线性矩阵不等式的解决方案，使得估计误差系统稳定，并且能够通过一个阈值检测故障。此外，还提供了一种自适应故障诊断方法来估计故障的大小。通过仿真验证了所提方法的有效性。
This paper presents a new fault detection and diagnosis (FDD) algorithm for general stochastic systems. Different from the classical FDD design, the distribution of system output is supposed to be measured rather than the output signal itself. The task of such an FDD algorithm design is to use the measured output probability density functions (PDFs) and the input of the system to construct a stable filter-based residual generator such that the fault can be detected and diagnosed. For this purpose, square root B-spline expansions are applied to model the output PDFs and the concerned problem is transformed into a nonlinear FDD algorithm design subjected to a nonlinear weight dynamical system. A linear matrix inequality based solution is presented such that the estimation error system is stable and the fault can be detected through a threshold. Moreover, an adaptive fault diagnosis method is also provided to estimate the size of the fault. Simulations are provided to show the efficiency of the proposed approach.