Establishment of fault diagnosis method based on time series data based according to Causal relationship extraction among the components of Building Air-conditioning System
| Project/Area Number |
16K00314
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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 |
Intelligent informatics
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| Research Institution | Kindai University |
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Principal Investigator |
YUMOTO Masaki 近畿大学, 理工学部, 准教授 (00304064)
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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 |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 異常診断 / 意思決定支援 / 測定値時系列データ / ビル空調システム / 定性値 / 決定ルール / 定性モデル / データセット / ラフ集合 / 組み合わせ / 因果関係 / 異常状態検知 / 情報システム / 知能情報処理 |
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| Outline of Final Research Achievements |
In a building air-conditioning system, measured time-series data is observed from many kinds of sensors. It is difficult to detect the fault by the administrators because only the limited experts can diagnose the unusual system. Thus, a new method is required, which can detect faults from measured data using computers automatically.
This research proposes the method of fault diagnosis with decision rules of rough set based on qualitative model of measured time-series data in building air- conditioning system. First, the proposal method converts target measured time-series data into data set based on target qualitative model. Next, this method constructs the decision rule of a rough set by comparison of the data set for every block. Finally, this method detects fault through comparison of evaluation values. Through practical experiments, it is confirmed that the proposal method can detect faults without expert knowledge in a building air-conditioning system.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究ではビル空調システムにおける測定値時系列データから求めるラフ集合の決定ルールを用いた異常診断方法を確立した。提案方法により専門家知識を用いずに異常状態が発生した区画を検知できる。また異常発生が特定できた場合に、異常状態を示す決定ルールにより異常状態の原因を特定できる。
本研究ではさらに、異常状態の特徴把握により異常検知に必要となる専門家の判断基準を自動的に求める方法を確立した。異常状態でのデータの特徴をラフ集合の決定ルールにより表現して判断基準とする方法により、新しい空調システムに対して従来は専門家が手作業で行っていた判断基準作成を、提案方法では異常状態の特定だけで自動的に抽出できる。
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Report
(4 results)
Research Products
(12 results)
