College of Electrical and Information Engineering, Lanzhou University of Technology, Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China.
ISA Trans. 2012 Nov;51(6):786-91. doi: 10.1016/j.isatra.2012.07.003. Epub 2012 Aug 14.
A new fault detection and diagnosis (FDD) problem via the output probability density functions (PDFs) for non-gausian stochastic distribution systems (SDSs) is investigated. The PDFs can be approximated by radial basis functions (RBFs) neural networks. Different from conventional FDD problems, the measured information for FDD is the output stochastic distributions and the stochastic variables involved are not confined to Gaussian ones. A (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. In this work, a nonlinear adaptive observer-based fault detection and diagnosis algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the fault. Stability and Convergency analysis is performed in fault detection and fault diagnosis analysis for the error dynamic system. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained.
研究了一种通过非高斯随机分布系统（SDS）的输出概率密度函数（PDF）进行故障检测和诊断（FDD）的新问题。PDF 可以通过径向基函数（RBF）神经网络来逼近。与传统的 FDD 问题不同，FDD 的测量信息是输出随机分布，所涉及的随机变量不仅限于高斯分布。提出了一种（RBFs）神经网络技术，使得输出 PDF 可以通过 RBFs 神经网络的动态权重来表示。在这项工作中，通过引入调整参数，提出了一种基于非线性自适应观测器的故障检测和诊断算法，使得残差对故障尽可能敏感。在故障检测和故障诊断分析中，对误差动态系统进行了稳定性和收敛性分析。最后，给出了一个实例来说明所提出算法的有效性，得到了令人满意的结果。
