1994 Fiscal Year Final Research Report Summary
Knowledge-aided Model-based Fault Diagnosis for Dynamic Systems with Time-varying Parameters
| Project/Area Number |
04452211
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
計測・制御工学
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
KUMAMARU Kousuke Kyushu Institute of Technology, Faculty Professor of Computer Science and Systems Engineering,, 情報工学部, 教授 (30037949)
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| Co-Investigator(Kenkyū-buntansha) |
GOTANDA Hiromu Kinki University in Kyushu, Faculty of Engineering Associate Professor, 九州工学部, 助教授 (10153751)
UCHINO Eiji Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineer, 情報工学部, 助教授 (30168710)
INOUE Katsuhiro Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineer, 情報工学部, 助教授 (00150516)
HIRAKI Naoji Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineer, 情報工学部, 教授 (30038559)
YAMAKAWA Takeshi Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineer, 情報工学部, 教授 (00005547)
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| Project Period (FY) |
1992 – 1994
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| Keywords | Fault Diagnosis / Modelling / Parameter Estimation / Nonlinear Systems / System Identification / Neural Networks / Thermal Power Plants / Knowledge |
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| Research Abstract |
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 Method
In 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 errors
To 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.
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