Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

Abstract

The current work is based on the development of an inverse approach in the domain of Recurrent Neural Networks (RNNs) to identify and quantify cracks on a multi-cracked cantilever beam structure subjected to transit mass. At first, the responses of the multicracked structure subjected to transit load are determined using fourth order Runge-Kutta numerical method and finite element analysis (FEA) has been executed using ANSYS software to authenticate the employed numerical method. The existences and positions of cracks are identified from the measured dynamic excitation of the structure. The crack severities are found out by FEA as forward problem. The modified Elman's Recurrent Neural Networks (ERNNs) approach has been implemented as inverse problem to predict the locations and severities of cracks in the structure by applying Levenberg-Marquardt (LM) back propagation algorithm. The present analogy has been carried out in a supervised manner to check the convergence of the proposed algorithm. The proposed ERNNs method converge good results with those of theory and FEA.

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

“…The authors have also compared the present results with their earlier works [12,13] and found that the present approach yields better results. It has been observed that the modified recurrent neural networks approach yield better accuracy as comparison to the individual ERNNs and JRNNs analogies.…”

“…The results obtained from the modified RNNs analogy vary with approximately 3.5% with the numerical values for relative crack locations while the results for crack severities vary about 3.9% approximately with the numerical values which are considered as good convergent. In the earlier works, the authors have also applied the analogy of the modified ERNNs [12] and JRNNs [13] based approaches individually with L-M back propagation algorithm.…”

“…The L-M (Levenberg-Marquardt) back propagation algorithm by Yu and Wilamski [21] is implemented to the proposed network to estimate the crack locations and depth of the structures. The training of the network model has been conducted with the same procedures of authors [12,13] earlier works and Yu and Wilamski [21]. Due to the fast and steady convergence properties, the L.M back propagation algorithm is applied for the proposed network model.…”

“…The numerical, FEA and experimental works which are already validated with each other earlier by the authors [8][9][10] are considered as forward problems, while the modified RNNs analogy is considered as inverse method in the present formulation. The present method has been also compared with author's previous works [12,13].…”

“…They have determined the structure's response at various damage and moving load parameters. In the later part, Jena and Parhi [12,13] have also developed damage detection procedures for structure under traverse load as inverse methods using the concepts of different types of rule-based recurrent neural networks. (ERNNs and JRNNs).…”