权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基于结构建模的非线性系统故障诊断方法研究

基本信息

批准号：
15560381
负责人：
KUMAMARU Kousuke
金额：
$ 2.05万
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 (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15560381/
关键词：
Fault Diagnosis Fault Detection Nonlinear Systems System Identification Structure Modeling Parameter Estimation Threshold Decision Ship Propulsion System モデルベース故障診断カルバック識別情報量闘値判定 FDI FDIシステム適応制御

项目摘要

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

在本研究计画中，我们针对非线性黑箱系统进行了基于模型的故障诊断方法的研究，并取得了以下成果：1.非线性系统辨识的准ARMAX模型的建立我们建立了具有与ARMAX模型相同线性结构的“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，将系统的非线性特征嵌入到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，由于其结构建模的特点，该模型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。通过对船舶推进系统模型的仿真研究，证实了该模型可以有效地用于非线性系统的故障检测，并将其作为基准试验的对象模型。 ...更多信息 rk测试故障诊断问题。2.基于拟ARMAX模型的故障检测特征提取利用已有的递推辨识方法（如预测误差法）对拟ARMAX模型的参数进行估计，并将正常运行期间辨识出的模型作为故障检测的参考模型。引入Kullback判别信息（KDI）作为故障检测指标。KDI是两个识别模型之间的失真度量，即正常操作下获得的参考模型和监测期间在线识别的工厂模型。为了建立高性能的故障检测系统，我们提出了几种改进方案的特征提取程序的基础上拟ARMAX建模和识别。通过对船舶推进基准系统的仿真研究，验证了该方案对各种故障模式的有效性。3.故障检测与隔离（FDI）系统的实现准ARMAX模型本质上是一种表示未知系统的输入输出型数学模型，因此其参数没有任何关于系统结构的物理信息。但由于模型具有带权因子的多线性形式，因此由于配置参数的变化而引起的被控对象故障模式的特征在一定程度上可以反映在辨识出的模型参数上。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，根据故障模式的先验知识构造出典型故障模式对应的参考特征集，利用模式识别方法进行故障隔离。在这种方式中，我们开发了一个基于模型的黑盒非线性系统的FDI系统，其有效性已通过仿真研究得到证实。在这些研究中，我们还证实了本研究项目的合作者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