書誌事項
- タイトル別名
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- Characterization of corrosive flaws by means of neural networks in the far-side magnetic flux leakage technique
- Far-side漏洩磁束探傷法におけるニューラルネットワークを用いたきずの定量的評価手法
- Far side ロウエイジソク タンショウホウ ニ オケル ニューラル ネットワーク オ モチイタ キズ ノ テイリョウテキ ヒョウカ シュホウ
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説明
The effective far-side magnetic flux leakage technique for the inspection on the bottom plates of oil tanks was proposed as a quantitative evaluation method on local corrosion. Based on the technique, the flaw characterization obtained from the inspection data under the saturation magnetization by using the neural network was presented, and its effectiveness has been proven in this paper. In addition, the practicality of the proposed neural network has been confirmed by its successful application to a large steel plate such as the bottom plates of oil tanks when original data was corrected with the result of the finite element simulation. Further improvement on the accuracy of quantitative evaluation on natural flaws can be expected by training neural network with different signals.
収録刊行物
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- 圧力技術
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圧力技術 43 (6), 327-334, 2005
一般社団法人 日本高圧力技術協会
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キーワード
詳細情報 詳細情報について
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- CRID
- 1390001204616944000
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- NII論文ID
- 10016866375
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- NII書誌ID
- AN00011762
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- ISSN
- 13479598
- 03870154
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- NDL書誌ID
- 7756328
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可
