On the way to fault detection method in moving load dynamics problem by modified recurrent neural networks approach

Abstract

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.

Investigation of Crack in Beam Structure using an Adaptive-Genetic Algorithm (AGA)

“…Pacheco-Chérrez, Cárdenas, and Probst (2021) applied the De-noising and wavelet transformation approach to find out and determine the dimension and orientation of crack-type damage in composite structures. Jena and Parhi (2021) also developed a crack detection procedure in the domain of RNNs for moving load dynamic problem. They have also verified the method with FEA.…”