A phase field and deep-learning based approach for accurate prediction of structural residual useful life

In this work, we proposed a novel approach for the prediction of residual useful life (RUL) of structures through appropriately combining the phase field method and deep-learning. In this new approach, the phase field method is firstly utilized to obtain the structural responses of crack growth, which are further preserved as images. Then, the convolutional neural network (CNN) is constructed to establish a predictive model. The proposed approach is a hybrid model of both physical and data-driven techniques, which can build a bridge between traditional computational fracture mechanics and deep learning algorithms. Several numerical cases are studied to evaluate the prediction performance of the proposed approach. The analysis results demonstrate that the present approach is able to predict the RUL of the structures with high level of accuracy. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study proposed a novel approach for predicting the residual useful life (RUL) of structures by combining the phase field method and deep learning. The hybrid model of physical and data-driven techniques can accurately predict the RUL of structures and bridge the gap between traditional computational fracture mechanics and deep learning algorithms.