Research on Fault Diagnosis Method for Nonlinear Systems Based on Their Structures' Modeling

在本研究项目中，我们对基于模型的非线性黑箱系统故障诊断方法的开发进行了研究，并取得了以下成果：1。一类用于辨识非线性系统的准ARMAX模型的发展我们开发了一种与ARMAX模型具有相同线性结构的准ARMAX模型，作为辨识非线性黑箱系统的一般输入-输出型模型。通过非线性非参数建模将系统非线性特性嵌入到ARMAX模型参数中，具有足够的灵活性来描述各种类型的非线性系统。此外，该模型由于其结构建模的特性，在线性系统理论框架下的系统分析和控制设计中具有广泛的适用性。通过对船舶推进装置模型的仿真研究，证实了该模型可以有效地用于非线性系统的故障检测，并提出了该模型作为故障诊断问题基准试验的装置模型。在植物模型中，各种故障模式可以通过植物配置参数的意外突变来实现。基于准armax模型的故障检测特征提取准armax模型的参数可采用已有的递归识别方案（如预测误差法）进行估计，并将正常运行期间识别出的模型作为故障检测的参考模型。引入Kullback判别信息（KDI）作为故障检测的指标。KDI是两个已识别模型（即正常运行时获得的参考模型和监测期间在线识别的工厂模型）之间的失真度量。为了建立高性能的故障检测系统，我们提出了几种基于准armax建模和识别的特征提取方法的改进方案。通过对船舶推进基准系统的仿真研究，验证了该方案在各种故障模式下的有效性。一个FDI （Fault Detection and Isolation）系统的实现准armax模型本质上是一种用于表示未知系统的输入-输出型数学模型，因此其参数不包含系统结构的任何物理信息。但由于该模型具有多线性形式，并带有加权因子，因此装置中由于配置参数变化而发生的故障模式特征可能会在一定程度上反映到已识别的模型参数上。基于这一思想，我们在故障检测系统中实现了故障隔离功能。故障检测完成后，在由模型参数组成的特征空间中采用模式识别方法进行故障隔离，该方法基于典型故障模式的先验知识构建典型故障模式对应的参考特征集。通过这种方法，我们开发了一种基于模型的黑箱非线性系统的FDI系统，并通过仿真研究证实了其有效性。在这些研究中，我们也证实了本项目同事开发的几种模式聚类和识别方法可以有效地用于构建FDI系统。少

In this research project, we have performed studies on the development of a Model-based Fault Diagnosis Method for nonlinear black-box systems and obtained following results.1.Development of A Quasi-ARMAX Modeling for Identification of Nonlinear SystemsWe developed as the general input-output type model for identifying nonlinear black-box systems "A Quasi-ARMAX Model" with the same linear structure as the ARMAX model. It has enough flexibility to describe various types of nonlinear systems by imbedding system nonlinear characteristics into the ARMAX model parameters through nonlinear nonparametric modeling. Furthermore the model has wide applicability to system analysis and control design in the framework of linear system theory due to its property of the structure modeling. It has been confirmed that the model can effectively be used for fault detection of nonlinear systems through simulation studies on the ship propulsion plant model, which was proposed as the plant model for benchma … More rk test of fault diagnosis problems. Here in the plant model, various fault mode could be realized as unexpected abrupt changes in plant configuration parameters.2.Feature Extraction for Fault Detection based on the Quasi-ARMAX ModelParameters of the Quasi-ARMAX Model could be estimated by using existing recursive identification scheme, e.g. prediction error method, and the model identified during the normal operating period was used as the reference model for fault detection. The Kullback Discrimination Information (KDI) was introduced as the index of fault detection. The KDI is a distortion measure between two identified models, i.e. the reference model obtained under normal operation and the on-line identified plant model during the monitored period. In order to establish the fault detection system with high performance, we have proposed several improvement schemes to feature extraction procedures based on the Quasi-ARMAX modeling and identification. The effectiveness of the schemes has been verified for various fault modes through simulation studies on the ship propulsion benchmark system.3.Realization of A FDI (Fault Detection and Isolation) SystemThe Quasi-ARMAX model is essentially an input-output type mathematical model for representing unknown system, therefore its parameters have not any physical information about the system structure. However the model has multi-linear form with weighting factors, so features of fault modes occurred in the plant due to changes in the configuration parameters may be reflected in some degree to the identified model parameters. Based on this idea, we have realized a fault isolation function in our fault detection system. After the fault detection, the fault isolation could be performed by using pattern recognition method in the feature space consisting of model parameters, in which reference feature sets corresponding to typical fault modes were constructed based on a prior knowledge about their fault modes. In this way we developed a model based FDI system for black box nonlinear systems and its effectiveness has been confirmed via the simulation studies. In these studies, we also confirmed that several methods of pattern clustering and recognition developed by co-workers of this research project could effectively be used to construct the FDI system. Less