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

Published on Sep 1, 2021in Computer Methods in Applied Mechanics and Engineering6.756
· DOI :10.1016/J.CMA.2021.113885
S.Z. Feng3
Estimated H-index: 3
(HEBUT: Hebei University of Technology),
Y. Xu1
Estimated H-index: 1
(Ocean University of China)
+ 2 AuthorsAtilla Incecik29
Estimated H-index: 29
(University of Strathclyde)
Abstract 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.
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Abstract Fault diagnosis enables to make savings related to maintenance. The presented work describes fault diagnosis method based on analysis of thermal images. An original method for feature extraction of thermal images BCAoID (Binarized Common Areas of Image Differences) is proposed. Thermal images of three electric impact drills (EID) were used for an analysis: healthy EID, EID with faulty fan (10 broken fan blades), EID with damaged gear train. Features of thermal images were extracted usin...
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#2Tran Minh Tu (National University of Civil Engineering)H-Index: 9
Last. T.T. Bui (University of Lyon)H-Index: 10
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This paper proposed a new approach in predicting the local damage of reinforced concrete (RC) panels under impact loading using gradient boosting machine learning (GBML), one of the most powerful techniques in machine learning. A number of experimental data on the impact test of RC panels were collected for training and testing of the proposed model. With the lack of test data due to the high cost and complexity of the structural behavior of the panel under impact loading, it was a challenge to ...
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#2Xu Han (HEBUT: Hebei University of Technology)H-Index: 58
Last. Zhen Li (Ocean University of China)H-Index: 1
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#2C.P. Gandhi (Rayat Bahra University)H-Index: 7
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#2Wei Zhou (WHU: Wuhan University)H-Index: 23
Abstract Phase-field models have become popular to simulate cohesive failure problems because of their capability of predicting crack initiation and propagation without additional criteria. In this paper, new phase-field damage model coupled with general softening law for cohesive fracture is proposed based on the unified phase-field theory. The commonly used quadratic geometric function in the classical phase-field model is implemented in the proposed model. The modified degradation function re...
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#2Jinhyun Choo (HKU: University of Hong Kong)H-Index: 17
Geologic shear fractures such as faults and slip surfaces involve marked friction along the discontinuities as they are subjected to significant confining pressures. This friction plays a critical role in the growth of these shear fractures, as revealed by the fracture mechanics theory of Palmer and Rice decades ago. In this paper, we develop a novel phase-field model of shear fracture in pressure-sensitive geomaterials, honoring the role of friction in the fracture propagation mechanism. Buildi...
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Abstract Magnetic Resonance Images (MRI) are often contaminated by rician noise at the acquisition time. This type of noise typically deteriorates the performance of disease diagnosis by a human observer or an automated system. Thus, it is necessary to remove the rician noise from MRI scans as a preprocessing step. In this letter, we propose a novel Convolutional Neural Network (CNN), viz. CNN-DMRI, for denoising of MRI scans. The network uses a set of convolutions to separate the image features...
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