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Jingjing He
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Year
A probabilistic crack size quantification method using in-situ Lamb wave test and Bayesian updating
J Yang, J He, X Guan, D Wang, H Chen, W Zhang, Y Liu
Mechanical Systems and Signal Processing 78, 118-133, 2016
1202016
Structural response reconstruction based on empirical mode decomposition in time domain
J He, X Guan, Y Liu
Mechanical Systems and Signal Processing 28, 348-366, 2012
1122012
A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves
J He, X Guan, T Peng, Y Liu, A Saxena, J Celaya, K Goebel
Smart Materials and Structures 22 (10), 105007, 2013
1022013
Equivalent surface defect model for fatigue life prediction of steel reinforcing bars with pitting corrosion
J Chen, B Diao, J He, S Pang, X Guan
International Journal of Fatigue 110, 153-161, 2018
722018
An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations
X Guan, J He, R Jha, Y Liu
Reliability Engineering & System Safety 97 (1), 1-13, 2012
682012
A fatigue crack size evaluation method based on lamb wave simulation and limited experimental data
J He, Y Ran, B Liu, J Yang, X Guan
Sensors 17 (9), 2097, 2017
582017
A Bayesian fusion method for composite damage identification using Lamb wave
H Huo, J He, X Guan
Structural Health Monitoring 20 (5), 2337-2359, 2021
472021
Microstructure evolution and mechanical properties in a gas tungsten arc welded Fe42Mn28Co10Cr15Si5 metastable high entropy alloy
J Shen, P Agrawal, TA Rodrigues, JG Lopes, N Schell, J He, Z Zeng, ...
Materials Science and Engineering: A 867, 144722, 2023
462023
Probabilistic fatigue damage prognosis of lap joint using Bayesian updating
T Peng, J He, Y Xiang, Y Liu, A Saxena, J Celaya, K Goebel
Journal of Intelligent Material Systems and Structures 26 (8), 965-979, 2015
412015
A Lamb wave quantification model for inclined cracks with experimental validation
HE Jingjing, HUO Haode, G Xuefei, Y Jinsong
Chinese Journal of Aeronautics 34 (2), 601-611, 2021
402021
A model assessment method for predicting structural fatigue life using Lamb waves
D Wang, J He, X Guan, J Yang, W Zhang
Ultrasonics 84, 319-328, 2018
402018
Lamb wave damage quantification using GA-based LS-SVM
F Sun, N Wang, J He, X Guan, J Yang
Materials 10 (6), 648, 2017
392017
Application of artificial neural networks for quantitative damage detection in unidirectional composite structures based on Lamb waves
C Qian, Y Ran, J He, Y Ren, B Sun, W Zhang, R Wang
Advances in Mechanical Engineering 12 (3), 1687814020914732, 2020
322020
A novel mode shape reconstruction method for damage diagnosis of cracked beam
J He, Y Zhou
Mechanical Systems and Signal Processing 122, 433-447, 2019
322019
In-situ SEM investigation and modeling of small crack growth behavior of additively manufactured titanium alloy
X Wang, Y Zhao, L Wang, L Wei, J He, X Guan
International Journal of Fatigue 149, 106303, 2021
312021
Life time extension of turbine rotating components under risk constraints: A state-of-the-art review and case study
X Guan, J He
International Journal of Fatigue 129, 104799, 2019
282019
Improve the accuracy of asymptotic approximation in reliability problems involving multimodal distributions
J He, X Guan, R Jha
IEEE Transactions on Reliability 65 (4), 1724-1736, 2016
282016
Probabilistic fatigue life prediction and structural reliability evaluation of turbine rotors integrating an automated ultrasonic inspection system
X Guan, J He, EM Rasselkorde, J Zhang, WA Abbasi, SK Zhou
Journal of Nondestructive Evaluation 33, 51-61, 2014
282014
An asymptotic stochastic response surface approach to reliability assessment under multi-source heterogeneous uncertainties
J He, M Huang, W Wang, S Wang, X Guan
Reliability Engineering & System Safety 215, 107804, 2021
272021
Probabilistic model updating for sizing of hole-edge crack using fiber bragg grating sensors and the high-order extended finite element method
J He, J Yang, Y Wang, H Waisman, W Zhang
Sensors 16 (11), 1956, 2016
272016
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