عیب یابی ارتعاشی پوسته های استوانه ای براساس انحنای شکل مودها

نوع مقاله : مقاله کوتاه

نویسندگان

• نیما جعفرزاده اقدم ¹
• محمد زهساز ²
• مرتضی صادقی ²

¹ دانشگاه ار وه ته ها آخن

² دانشگاه تبریز

10.22067/fum-mech.v30i1.58045

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

• پوسته استوانه ای
• انحنای شکل مود
• تحلیل المان محدود
• شاخص عیب
• شبکه عصبی مصنوعی

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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

مراجع

1. Qiao, P., Lu, K., Lestariand, W. and Wang, J. "Curvature Mode Shape-based Damage Detection in Composite Laminated Plates", Composite Structures, Vol. 80, No. 3, pp. 409-428, (2007).

2. Fan, W. and Qiao, P., "Vibration-based Damage Identification Methods: A Review and Comparative Study", Structural Health Monitoring, Vol. 10, No. 1, pp. 83-111, (2011).

3. Torabi, K., Shariati-Nia, M. and Heidari-Rarani, M., "Moving Support Technique for Delaminatoin Detection in Laminated Composite Beams Using the First Natural Frequency", Journal of Reinforced Plastics and Composites, pp. 1116-1128, 0731684417703489, (2017).

4. Torabi, K., Shariati-Nia, M. and Heidari-Rarani, M., "Experimental and Theoretical Investigation on Transverse Vibration of Delaminated Cross-ply Composite Beams", International Journal of Mechanical Sciences, Vol. 115, pp. 1-11, (2016).

5. Dessi, D. and Camerlengo, G., "Damage Identification Techniques Via Modal Curvature Analysis: Overview and Comparison", Mechanical Systems and Signal Processing, Vol. 52, pp. 181-205, (2015).

6. Giurgiutiu, V., "Structural Health Monitoring (SHM) of Aerospace Composites", Polymer Composites in the Aerospace Industry, pp. 449–507, (2015).

7. Qiao, P., Lestari, W., Shah, M.G. and Wang, J. "Dynamics-based Damage Detection of Composite Laminated Beams Using Contact and Noncontact Measurement Systems", Journal of Composite Materials, Vol. 41, No. 10, pp. 1217-1252, (2007).

8. He, M., Yang, T. and Du, Y.,"Nondestructive Identification of Composite Beams Damage Based on the Curvature Mode Difference", Composite Structures, Vol. 176, pp. 178-186, (2017).

9. Xu, Y., Zhu, W. and Smith, S., "Non-model-based Damage Identification of Plates Using Principal, Mean and Gaussian Curvature Mode Shapes", Journal of Sound and Vibration, Vol. 400, pp. 626-659, (2017).

10. Xiang, J., Matsumoto, T., Wang, Y. and Jiang, Z., "Detect Damages in Conical Shells Using Curvature Mode Shape and Wavelet Finite Element Method", International Journal of Mechanical Sciences, Vol. 66, pp. 83-93, (2013).

11. ASTM A106 / A106M, Standard Specification for Seamless Carbon Steel Pipe for High-Temperature Service, ASTM International, West Conshohocken, PA, (2010), www.astm.org

12. Ratcliffe, C.P., "A Frequency and Curvature Based Experimental Method for Locating Damage in Structures", Journal of Vibration and Acoustics, Vol. 122, No. 3, pp. 324-329, (2000).

13. Paya, B., Esat, I. and Badi, M., "Artificial Neural Network Based Fault Diagnostics of Rotating Machinery Using Wavelet Transforms as a Preprocessor", Mechanical Systems and Signal Processing, Vol. 11, No. 5, pp. 751-765, (1997).

14. Hagan, M.T., Demuth, H.B. and Beale, M.H., "Neural Network Design", Pws Pub. Boston, (1996).