Knowledge-aided Model-based Fault Diagnosis for Dynamic Systems with Time-varying Parameters

本课题主要研究了基于知识辅助的时变参数动态系统模型诊断方法的发展，取得了以下研究成果：1.快速辨识方法的发展及其在故障诊断和自适应控制中的应用本文提出了一种快速系统辨识方法SANK方法，该方法可以利用输入输出数据的短时记录在线估计连续时间非线性系统的时变参数。该方法基于对描述目标系统的仿真器的参数调整。它可以用于检测和隔离故障所造成的意外变化的系统配置参数，以及用于自适应控制的未知时变系统。通过对火电厂和伺服驱动系统的应用研究，证实了该方法的有效性。2.基于知识辅助的模型诊断方法研究为了判断Kullback判别信息（KDI）检测到的系统变化是由于故障还是正常运行，通过将系统运行模式和故障特征的知识信息结合到学习过程中，建立了神经网络决策系统。通过自适应控制二阶阻尼振子故障诊断的仿真研究，验证了知识辅助诊断方法的有效性。3.对建模误差具有鲁棒性的基于模型的诊断方法的研究为了实现实用的基于模型的诊断方法，本文研究了非线性系统的一般建模方法（拟ARMAX模型），提出了一种鲁棒辨识方法。然后，我们建立了一个计划，鲁棒故障检测的动态系统的不确定性，使用一种新的方法来评估建模误差。

In this research project, we have performed studies on the development of a Knowledgeaided Model-based Diagnosis Method for Dynamic Systems with Time-varying Parameters and obtained following research results.1.Development of a Quick Identification Method and Its Applications to Fault Diagnosis and Adaptive Control.We developed a quick system identification method named as SANQ method, which can estimate on-line time-varying parameters in continuous-time nonlinear systems by using short time record of input and output data. The method is based on the parameter adjustment of simulator describing the objective systems. It can be used for detection and isolation of faults which are caused by unexpected changes in system configuration parameters, and are used for the adaptive control of unknown time-varying systems as well. The effectiveness of the method has been confirmed through the application studies to thermal power plants and servo-driving systems.2.Research on a Knowledge-aided Model-based Diagnosis MethodIn order to decide whether the system change detected by Kullback discrimination information (KDI) is due to a fault or is under the normal operation, a neural network decision system has been established by incorporating knowledge information on the system operating modes and fault characteristics into the learning processes. The effectiveness of the knowledge-aided diagnosis method has been confirmed trough the simulation studies on fault decision of the 2nd order damped oscillator which is controlled by an adaptive way.3.Investigation on the model-based diagnosis method which is robust to modelling errorsTo realize a practical model-based diagnosis method, we investigated on a general modeling (Quasi-ARMAX Models) for nonlinear systems and proposed an robust identification method. We have then established a scheme to robust fault detection for dynamic systems with uncertainty by using a new method for evaluating modelling errors.