【摘要】 随着传感器、图像和数据处理等技术的发展,在炼钢、造纸和化工过程中,反馈量测信息是系统的在线随机输出分布,而并非输出变量在样本时刻的量值.不同于传统的故障检测和诊断(fault detection and diagnosis,FDD)问题,对于一类复杂随机工程系统,基于随机分布函数研究了一种新的优化FDD方法.利用B-样条逼近将输出概率密度函数(PDF)转化成动态权系数,进而建立了带有时滞的非线性不确定性权动态模型,将相应的FDD问题转化成一类非线性不确定时滞系统的FDD问题,基于线性矩阵不等式得到了可行的优化故障检测方法和广义自适应故障诊断算法.仿真实例也表明了该算法的有效性.更多还原

【Abstract】 With the rapid advances in sensor technology, image manipulation and data processing, for some practical processes such as steel-making, paper-making and chemical processing, the feedback measurement information is the stochastic distribution of the system output rather than its value. Different from classical fault detection and diagnosis (FDD) problems, a novel optimal FDD scheme based on stochastic distribution functions is studied for a type of complex engineering systems. B-spline expansion technique is applied so that the output probability density function (PDF) can be formulated in terms of the dynamic weightings. Furthermore, a dynamic model correlating the input and the weightings is established, where nonlinearities, uncertainties and time delays are included. As a result, the concerned FDD problem is transformed into a classical nonlinear FDD problem subject to an uncertain nonlinear system with time delays. Feasible optimization criteria to detect the system fault are obtained and an adaptive fault diagnosis algorithm is further presented to measure the size of fault. Simulations are given to demonstrate the efficiency of the proposed approach.更多还原