2005 Fiscal Year Final Research Report Summary
Research on Fault Diagnosis Method for Nonlinear Systems Based on Their Structures' Modeling
| Project/Area Number |
15560381
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Control engineering
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
KUMAMARU Kousuke Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineering, Professor, 情報工学部, 教授 (30037949)
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| Co-Investigator(Kenkyū-buntansha) |
INOUE Katsuhiro Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineering, Associate Professor, 情報工学部, 助教授 (00150516)
MAEDA Makoto Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineering, Research Assistant, 情報工学部, 助手 (00274556)
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| Project Period (FY) |
2003 – 2005
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| Keywords | Fault Diagnosis / Fault Detection / Nonlinear Systems / System Identification / Structure Modeling / Parameter Estimation / Threshold Decision / Ship Propulsion System |
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| Research Abstract |
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 Systems
We 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
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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 Model
Parameters 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) System
The 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
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Research Products
(28 results)
