著者

嶺 竜治 亀山 達也 高橋 寿一 古賀 昌史 緒方 日佐男

出版者

一般社団法人電子情報通信学会

雑誌

電子情報通信学会論文誌. D, 情報・システム (ISSN:18804535)

巻号頁・発行日

vol.92, no.6, pp.868-875, 2009-06-01

被引用文献数

1

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文字認識と単語レイアウト解析技術を用いて,紙文書の文字情報とサイバー空間のディジタル情報のリンクを張る手法を提案する.これまでに,WebサイトのURLが埋め込まれた二次元コードをカメラ付き携帯電話で認識し,そのWebサイトへ接続する技術が実用化されている.しかしながら,二次元コードを印刷するために文書レイアウトが制約を受けたり,印刷する二次元コードの数に制限があるという問題があった.提案する手法は,文書やページごとに紙面上の単語の並びが異なることに着目し,認識した複数の単語の相対位置関係を解析し,文書の種別や読取り位置を特定する手法である.そして,読取り位置とURLを対応づけたデータベースを作成しておけば,二次元コード等を文書に印刷することなくハイパリンクが実現できる.文書数4種,総単語数約9400の文書テキストデータを用いた原理実験で,97%の精度で文書種の特定を,また文書種が特定できれば約98%の精度で正しい単語位置を特定できることが分かった.また,カメラ付き携帯電話とPCを用いたプロトタイプを開発し,実際の携帯電話通信網での動作を確認した.今後は実験の規模を拡大するとともに,本方式を用いた様々なサービスの検討を行う予定である.

著者

竹田 憲生 亀山 達也

出版者

一般社団法人 日本機械学会

雑誌

日本機械学会論文集 (ISSN:21879761)

巻号頁・発行日

vol.88, no.910, pp.22-00095, 2022 (Released:2022-06-25)

参考文献数

14

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A practical structural health monitoring has been proposed for evaluating the structural health of a whole mechanical asset by using digital twin with data collected during the operation of the asset. Digital twin can be utilized to predict the remaining useful life by estimating the variation of the physical quantity that dominates the life, even though any records of failure do no exist. However, a mechanical asset includes huge number of local hot spots where structural health should be evaluated, and accordingly, huge man-hours are required to integrate a monitoring system that evaluates the health at all the hot spots by using digital twin. A hierarchical structural health monitoring has been therefore developed to overcome this drawback. In the first stage of the health monitoring, the overview of the mechanical damage of the components included in a asset is evaluated according to a metric, D factor, that defines the cumulative damage of the components, and the assets having relatively large damage are extracted. The extracted assets are then evaluated in detail in the second stage; that is, structural health is checked at the local hot spots. The monitoring system that employs digital twin and the hierarchical health monitoring was applied to the health management of wind turbines. As the result of evaluating the structural health of the main components of wind turbines, about a hundred wind turbines can be prioritized according to the D factor. In this first stage, a surrogate model based on a machine learning was utilized for evaluating the overview of the damage with low computational cost; the approximation error of the D factor was less than 3 % by using the surrogate model. It can be therefore concluded that this practical structural health monitoring is useful for the decision making of fleet health management of mechanical assets.

著者

竹田 憲生 亀山 達也

出版者

一般社団法人 日本機械学会

雑誌

日本機械学会論文集 (ISSN:21879761)

巻号頁・発行日

pp.22-00095, (Released:2022-06-07)

参考文献数

14

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A practical structural health monitoring has been proposed for evaluating the structural health of a whole mechanical asset by using digital twin with data collected during the operation of the asset. Digital twin can be utilized to predict the remaining useful life by estimating the variation of the physical quantity that dominates the life, even though any records of failure do no exist. However, a mechanical asset includes huge number of local hot spots where structural health should be evaluated, and accordingly, huge man-hours are required to integrate a monitoring system that evaluates the health at all the hot spots by using digital twin. A hierarchical structural health monitoring has been therefore developed to overcome this drawback. In the first stage of the health monitoring, the overview of the mechanical damage of the components included in a asset is evaluated according to a metric, D factor, that defines the cumulative damage of the components, and the assets having relatively large damage are extracted. The extracted assets are then evaluated in detail in the second stage; that is, structural health is checked at the local hot spots. The monitoring system that employs digital twin and the hierarchical health monitoring was applied to the health management of wind turbines. As the result of evaluating the structural health of the main components of wind turbines, about a hundred wind turbines can be prioritized according to the D factor. In this first stage, a surrogate model based on a machine learning was utilized for evaluating the overview of the damage with low computational cost; the approximation error of the D factor was less than 3 % by using the surrogate model. It can be therefore concluded that this practical structural health monitoring is useful for the decision making of fleet health management of mechanical assets.