| Project/Area Number |
03555092
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| Research Category |
Grant-in-Aid for Developmental 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 | Kure National College of Technology |
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Principal Investigator |
KOBAYASI Yasuhide Kure National College of Technology, Department of Electrical Engineering, Associate Professor, 電気工学科, 助教授 (10127764)
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| Co-Investigator(Kenkyū-buntansha) |
HASHIMOTO Hajime OSHIMA National College of Maritime Technology, Department of Information, Assoc, 情報工学科, 助教授 (60127777)
OKITA Tsuyoshi Yamaguchi University, Faculty of Engineering, Professor, 工学部, 教授 (70034345)
木梨 博文 日立製作所, 大みか工場
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| Project Period (FY) |
1991 – 1993
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| Project Status |
Completed (Fiscal Year 1993)
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| Budget Amount *help |
¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1993: ¥300,000 (Direct Cost: ¥300,000)
Fiscal Year 1992: ¥300,000 (Direct Cost: ¥300,000)
Fiscal Year 1991: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | Abnormal Diagnosis / System Identification / Knowledge Engineering / Modeling |
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| Research Abstract |
In order to do an abnormal diagnosis, 1) abnormal detection, 2) abnornmal identification, 3) abnormal prediction, 4) presentation of a recovery law, of four processes are necessary. At first as establishing a various sensor against a system, in order to do a detection of abnormally of it, We measure a physical quantity, chemical qunatity in various ways and compare the state variables of a normal system. Then the model regarding a normal and an abnormal system is necessary. and it is the most important at an abnormal diagnosis to model the system exactly.
We considered a following problem, at this research about each process 1)-4). 1) Detection law of parameter change for an already known of abnormal model structure. 2) Decision law of the most suitable input for a system identification. 3) Identificatin of an abnormal model against a on-linear system. 4) Identification law of the non-linear system to apply a knowledge base.
Because a system that we are aimed at is a complicated non-linear system in large way, structure of an abnormal model is unknown. Therefore, we asume it, as structure of an abnormal model is unknown and consider a system identification. We propose a method that we detect early as much as possible to abnormally. We were able to make an abnormal diagnosis caability against a large scale system improve.
We sum up a result of these and would like to construct the abnormal diagnosis ytem that is based on knowledge information.
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