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Experimental and finite-element free vibration analysis and artificial neural network based on multi-crack diagnosis of non-uniform cross-section beam | ||
| Journal of Computational & Applied Research in Mechanical Engineering (JCARME) | ||
| مقاله 1، دوره 5، شماره 1، اسفند 2015، صفحه 1-11 اصل مقاله (1.41 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jcarme.2015.340 | ||
| نویسندگان | ||
| B. Asmar1؛ M. Karimi* 1؛ F. Nazari1؛ A. Bolandgerami2 | ||
| 1Department of Mechanical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran | ||
| 2Center of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran | ||
| تاریخ دریافت: 02 اسفند 1393، تاریخ بازنگری: 29 فروردین 1394، تاریخ پذیرش: 06 اردیبهشت 1394 | ||
| چکیده | ||
| Crack identification is a very important issue in mechanical systems, because it is a damage that if develops may cause catastrophic failure. In the first part of this research, modal analysis of a multi-cracked variable cross-section beam is done using finite element method. Then, the obtained results are validated usingthe results of experimental modal analysis tests. In the next part, a novel procedure is considered to identify the locations and depths of cracks in the multi-cracked variable cross-section beam using natural frequency variations of the beam based on artificial neural network and particle swarm optimization algorithm. In the proposed crack identification algorithm, four distinct neural networks are employed for the identification of locations and depths of both cracks. Back error propagation and particle swarm optimization algorithms are used to train the networks. Finally, the results of these two methods are evaluated. | ||
| کلیدواژه‌ها | ||
| Modal analysis؛ Multiple crack identification؛ Variable cross section beam؛ Artificial neural network | ||
| مراجع | ||
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