CONSTRUCTION OF MODEL-BASED FAULT DIAGNOSIS SYSTEMS ROBUST TO MODELING ERROR

构建对模型误差具有鲁棒性的基于模型的故障诊断系统

• 批准号: 08455199
• 负责人: KUMAMARU Kousuke
• 金额: $ 4.35万
• 依托单位: KYUSHU INSTITUTE OF TECHNOLOGY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 1996
• 资助国家: 日本
• 起止时间: 1996 至 1998
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08455199/
• 关键词: Fault Diagnosis; Modeling; Nonlinear Systems; System Identification; Robust Fault Diagnosis; Threshold Decision; Parameter Estimation; Kullback Discrimination Information; 闘値判定

In this research project, for the purpose of construction of model-based fault diagnosis systems which are robust against to modeling error, we have investigated on the following subjects : 1). modeling and identification method for fault diagnosis of nonlinear dynamic systems, 2). evaluation method of model uncertainty(i.e. modeling error), 3). Fault detection scheme which is robust against to the modeling error, and 4). fault isolation scheme using knowledge information about the system to be diagnozed. The main results obtained for each subject arc as follows :1). We have proposed a Quasi-ARMAX model which is equiped with both of flexibility and linear structure, by imbedding nonlinear characteristics of the system into the ARMAX model parameters, and developed its identification method. It has been confirmed through simulation studies that the model is useful for the fault detection in wide class of nonlinear systems and for STR-based adaptive control as well.2). We have proposed a … More method for evaluating the modeling error due to linear approximation of nonlinear systems via the nonlinear terms in the Quasi-ARMAX modeling. Such the evaluation of modeling error is essential for the design of robust fault detection.3). We have analysed the relation between the modeling error and the Kullback Discrimination Information (KDI) which is used as the fault detection index, and developed a robust fault detection scheme based on the evaluation of modeling error. Another way of robust fault detection in nonlinear systems has been proposed based on multi-ARMAX models in the framework of the Quasi-ARMAX modeling and based on the analysis of the KDI index using the weighting coefficients of the multi-modeling. On the other hand, it is important issue to determine a resonable threshold value in the thrshold decision for fault detection. We have solved this problem by learning the probability density function of the KDI index using the data obtained from online identification of the system under the normal operation mode. As the result, a threshold value corresponding to a confidence revel of false alarm rate has been able to be detemined based on the learned probability density function of the KDI.Finally, as to the 4) research subject, it is still open problem due to the difficulty to estimate the physical parameters of the system through the Quasi-ARMAX modeling and identification procedures. Less

在本研究计画中，为了建构对模型误差具有鲁棒性的基于模型的故障诊断系统，我们研究了以下主题：1）。非线性动态系统故障诊断的建模与辨识方法。模型不确定性（即建模误差）的评估方法; 3）.对建模误差具有鲁棒性的故障检测方案; 4）.故障隔离方案使用关于待诊断系统的知识信息。主要结果如下：1）。通过将系统的非线性特性嵌入ARMAX模型参数中，提出了一种兼具柔性和线性结构的拟ARMAX模型，并给出了其辨识方法。仿真研究表明，该模型不仅适用于大类非线性系统的故障检测，而且适用于基于STR的自适应控制.我们提出了一种 ...更多信息 利用拟ARMAX模型中的非线性项对非线性系统进行线性逼近所引起的建模误差进行估计的方法。这种建模误差的评估对于鲁棒故障检测器的设计是必不可少的。分析了建模误差与作为故障检测指标的Kullback判别信息（KDI）之间的关系，提出了一种基于建模误差评估的鲁棒故障检测方案。在准ARMAX模型框架下，基于多模型权系数对KDI指标的分析，提出了非线性系统鲁棒故障检测的另一种方法。另一方面，在故障检测的阈值判决中，确定合理的阈值是一个重要的问题。我们已经解决了这个问题，通过学习的概率密度函数的KDI指数使用的数据从在线识别系统的正常操作模式下。最后，对于4）研究课题，由于采用拟ARMAX建模和辨识方法难以估计系统的物理参数，因此该问题仍然是一个待解决的问题。少

项目成果

K.Kumamaru, J.Hu, K.Inoue and T.Soderstrom: "A Method of Robust Fault Detection for Dynamic Systems by Using Quasi-ARMAX Modeling" Proc.of the 11 th IFAC Symposium on System Identification. 1157-1162 (1997)

K.Kumamaru、J.Hu、K.Inoue 和 T.Soderstrom：“使用准 ARMAX 建模对动态系统进行鲁棒故障检测的方法”第 11 届 IFAC 系统辨识研讨会论文集。

J.Hu, K.Hirasawa and K.Kumamaru: "Fuzzy Models Embedding STR Controller for Nonlinear Stochastic Systems" Proc.of the 29 th ISCIE International Symposium on Stochastic Systems Theory and Its Applications. Nov.10-12. 51-56 (1997)

J.Hu、K.Hirasawa 和 K.Kumamaru：“非线性随机系统的模糊模型嵌入 STR 控制器”第 29 届 ISCIE 国际随机系统理论及其应用研讨会论文集。

J.Hu, K.Hirasawa and K.Kumamaru: "A learning Network Based Modeling Scheme for Nonlinear Black-Box Systems" Proc.of the 7 th Intelligent System Symposium on Fuzzy, Artificial Intelligence, Neural Networks and Complex Systems. 217-222 (1997)

J.Hu、K.Hirasawa 和 K.Kumamaru：“基于学习网络的非线性黑盒系统建模方案”第七届模糊、人工智能、神经网络和复杂系统智能系统研讨会论文集。

K.Kumamaru, K.Inoue and S.Furukawa: "A Method for Threshold Setting in KDI-Based Robust Fault Detection of Nonlinear Systems" Proc.of the 30 th ISCIE International Symposium on Stochastic Systems Thory and Its Applications. Nov.4-6. (1998)

K.Kumamaru、K.Inoue 和 S.Furukawa：“基于 KDI 的非线性系统鲁棒故障检测中的阈值设置方法”第 30 届 ISCIE 国际随机系统理论及其应用研讨会论文集。

K.Kumamaru: "Chapter entitled “Statistical Methods for Robust Change Detection in Dynamical Systems with Model Uncertainty"" Marcel Dekker Inc.: Book entitled “Statistical Methods in Control and Signal Processing"(in press), (1997)

K.Kumamaru：“章节标题为“具有模型不确定性的动态系统中鲁棒变化检测的统计方法””Marcel Dekker Inc.：题为“控制和信号处理中的统计方法”的书（正在出版），（1997 年）