عیب یابی ارتعاشی پوسته های استوانه ای براساس انحنای شکل مودها

نوع مقاله : مقاله کوتاه

نویسندگان

1 دانشگاه ار وه ته ها آخن

2 دانشگاه تبریز

چکیده

در حال حاضر مطالعات اندکی به عیب­یابی لوله­ها و پوسته­های استوانه­ای جدارنازک اختصاص یافته‌است. در سا‌ل‌های اخیر انحنای شکل مودها به‌علت حساسیت بالا به عیب، مورد‌توجه محققان قرار گرفته‌است ولی این روش­ها نیازمند داده­برداری متراکم و اسکن ارتعاشی کل سطح می­باشند که این موضوع استفادۀ صنعتی از این روش­ها را بسیار محدود کرده­است. در این پژوهش روش جدیدی برای عیب­یابی پوسته­های استوانه­ای معرفی گردیده‌است که نیازمند داده سازه سالم نمی­باشد و داده­برداری صرفاً در سه راستای طولی پوسته انجام می­شود. با انتقال اطلاعات آنالیز ارتعاشی سازه به شبکۀ عصبی، خروجی شبکه مکان دقیق عیب در سطح سازه خواهد بود و به تکنسین ماهر برای تفسیر داده­ها نیاز ندارد. عملکرد روش پیشنهادی بااستفاده از ورودی­های جدید و غیر آشنا برای شبکه، صحت سنجی گردید و میزان رگرسیون 97/0 به‌دست آمد.
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کلیدواژه‌ها


عنوان مقاله [English]

Damage Identification of Cylindrical Shells Based on Mode Shapes Curvature

نویسندگان [English]

  • Nima Jafarzadeh Aghdam 1
  • Mohammad Zehsaz 2
  • Morteza Sadeghi 2
1 RWTH Aachen University
2 University of Tabriz
چکیده [English]

Currently there are just‫ a few papers about‫ fault detection of tubes and cylindrical shells. Recently, methods based on modal curvatures have gained great attention due to their sensitivity to defects. But the methods require dense number of data extraction points which limits their industrial application. In this research a new method for damage identification of cylindrical shells has been introduced. The method does not need intact structure's data and in contrast to other methods requires a few data extraction points. By transferring the modal information of the structure to the ANN, output of the network is exact position of the defect on the structure and the method does not need skilled technician to interpret the data. Performance of the network is validated by unfamiliar data for the network and 0.97 regression is obtained.

کلیدواژه‌ها [English]

  • Cylindrical Shell
  • Curvature Mode Shape
  • Finite Element Analysis
  • Damage Index
  • Artificial neural network
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