Early fault detection and fault tolerant control schemes combining operator, adaptive method with learning method
| Project/Area Number |
17K06225
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Dynamics/Control
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| Research Institution | Tokyo University of Agriculture and Technology |
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Principal Investigator |
Deng Mingcong 東京農工大学, 工学(系)研究科(研究院), 教授 (20295124)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 早期故障診断 / 早期故障耐性制御 / 機械学習法 / 適応手法 / オペレータ法 / 非線形制御 / プラント実験装置 / 早期故障検出 / 早期耐故障制御 / 機械力学・制御 |
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| Outline of Final Research Achievements |
In this research, early fault detection and fault tolerant control schemes were proposed by combining operator based method, adaptive method with learning based Support Vector Machine(SVM), One-class SVM and Generalized Gaussian function bsed SVM. The proposed design schemes were validated through simulations and experiments for nonlinear systems.
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| Academic Significance and Societal Importance of the Research Achievements |
産業界のみに限らず、安全性向上は最重要課題であり、そのための早期故障検出は強く求められており、本研究では、不確かな非線形システムに対して早期故障時を検出する実現可能な手法である。産業界などへの適用に適した手法で、ここに大きなインパクトを与えると期待する。
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Report
(4 results)
Research Products
(72 results)
