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

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.

言及状況

外部データベース (DOI)

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機械のデジタルコピーを計算機で稼働させるデジタルツイン。大規模でない現実的システムで実現する方法を提案してます。 実運用に向けて現在奮闘中。熱意で最後まで完成させます。 #エンジニア #プログラミング https://t.co/Bm8t9YG1zP
機械のデジタルモデルで健全性を判定し、メンテナンスの優先順位を決定する デジタルツイン。 実現に向けた課題の1つは、機械1台でも評価対象が数千〜数万になり、安いコストでシステムが実現できないこと。その解決方法を提案しています。 #エンジニア https://t.co/Bm8t9YG1zP
デジタルツインの論文が正式公開。機械を長く使うため、健全性を把握しようという試み。延長利用の判断指標になれば良いかと。PDFダウンロード可、ぜひ。 #エンジニア #プログラミング #dx デジタルツインによる機器の健全性管理を実現する階層型構造ヘルスモニタリング https://t.co/Bm8t9YX4BP

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