| Project/Area Number |
16K06418
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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 |
Control engineering/System engineering
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
Nishida Takeshi 九州工業大学, 大学院工学研究院, 准教授 (30346861)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | particle filter / feedback system / state observer / パーティクルフィルタ / 異常の定量化 / 状態量推定 / 状態フィードバック / オブザーバ / 故障検出 / ノイズの確率分布 / リサンプリング / 故障検知・隔離・回復 / ロバス ト残差推定 |
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| Outline of Final Research Achievements |
On the state estimation of a system including stochastic noises, particle filter (PF: particle filter) discretely approximates its filter distribution by using state hypotheses (particles) of many states, so it is possible to estimate the state of the system including non-Gaussian noise.In this research, we proposed a method to simultaneously execute estimation of control system state and quantification of abnormality by using PF as an observer of state feedback control system. Specifically, we proposed a method to detect sensor abnormalities by quantifying the deviation of the control system's behavior from a given model by monitoring the particle distribution of the PF incorporated into the control system as an observer.
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| Academic Significance and Societal Importance of the Research Achievements |
自動化が進む産業機器やインフラ設備において故障検出技術は重要である．しかし従来技術でば，対象とする機器の自動化装置や制御装置とは別に，あらたに故障検出のための機器を設置する必要があるため，高額な費用や故障検出器自体のメンテナンスが発生する．一方で，本研究成果を活用すれば，すでに搭載されている制御装置内の伝達ノイズを監視するだけで故障が可能になる．したがって，本研究成果は，広く利用されている機器の安全性を保障するために有用な基礎技術であると考えられる．
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