Mechanics & Industry
Volume 22, 2021
|Number of page(s)||8|
|Published online||08 March 2021|
On the way to fault detection method in moving load dynamics problem by modified recurrent neural networks approach
Department of Mechanical Engineering, Vardhaman College of Engineering, Hyderabad, India
2 Department of Mechanical Engineering, National Institute of Technology, Rourkela, India
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Accepted: 30 January 2021
Parameters identification on structure subjected to moving load can be predicted by using the accurate and reliable data. The concepts of recurrent neural networks (RNNs) approach have been used in parameters (crack locations and severities) identifications in structure subjected to moving load in the present methodology. This methodology has incorporated the knowledge based Elman's recurrent neural networks (ERNNs) and Jordan's recurrent neural networks (JRNNs) jointly for the identification of parameters. This approach has been addressed as the inverse problem for predicting the locations and quantification of cracks in the structure in a supervised manner. The Levenberg-Marquardt's back propagation algorithm is implemented to train the proposed networks. To check the robustness of the present method, Numerical studies followed by Finite Element Analysis (FEA) and experimental verifications (Forward problems) are presented as a case study by considering a multi-cracked simply supported structure under a moving mass. The estimated crack locations and severities obtained from the proposed RNNs model converge well with those of FEA and experiments. From the demonstration of the case study, it concludes that the proposed analogy can identify and quantify the crack locations and severities effectively.
Key words: Crack locations / crack severities / L-M algorithm / ERNNs / JRNNs
© S.P. Jena and D.R. Parhi, Hosted by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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