Quantum mechanics

Algorithm

Residual

Phase (matter)

Pure mathematics

Field (mathematics)

Bridge (graph theory)

Computer science

Physics

Organic chemistry

Pattern recognition (psychology)

Mathematics

Internal medicine

Artificial intelligence

Medicine

Chemistry

Deep learning

Artificial neural network

Convolutional neural network

Machine learning

Computer Methods in Applied Mechanics and Engineering

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.

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