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

在本研究项目中，为了构建对建模误差具有健壮性的基于模型的故障诊断系统，我们进行了以下几个方面的研究：1)。2)非线性动态系统故障诊断的建模与辨识方法。3)模型不确定度(即建模误差)的评定方法。对建模误差具有健壮性的故障检测方案；使用有关待诊断系统的知识信息的故障隔离方案。各课题的主要成果如下：1)。通过在ARMAX模型参数中嵌入系统的非线性特性，提出了一种兼具柔性和线性结构的准ARMAX模型，并给出了其辨识方法。仿真研究表明，该模型不仅适用于各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。我们已经提出了一项…通过准ARMAX建模中的非线性项来评估非线性系统的线性逼近建模误差的更多方法。这样的建模误差评估对于设计稳健的故障检测是必不可少的。分析了建模误差与作为故障检测指标的KDI值之间的关系，提出了一种基于建模误差评估的稳健故障检测方案。在准ARMAX建模框架下，利用多ARMAX模型的加权系数分析KDI指标，提出了另一种基于多ARMAX模型的非线性系统鲁棒故障检测方法。另一方面，确定合理的阈值是故障检测决策中的一个重要问题。通过使用正常运行模式下系统在线辨识获得的数据学习KDI指标的概率密度函数，解决了这一问题。最后，对于研究对象，由于很难通过准ARMAX建模和辨识过程来估计系统的物理参数，因此仍然是一个悬而未决的问题。较少

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