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Xiang Jiawei
Xiang Jiawei
Professor of Mechanical Engineering, Wenzhou University
Verified email at wzu.edu.cn
Title
Cited by
Cited by
Year
Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system
Y Wang, R Markert, J Xiang, W Zheng
Mechanical Systems and Signal Processing 60, 243-251, 2015
5602015
Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications
Y Wang, J Xiang, R Markert, M Liang
Mechanical Systems and Signal Processing 66, 679-698, 2016
5342016
Convolutional neural network-based hidden Markov models for rolling element bearing fault identification
S Wang, J Xiang, Y Zhong, Y Zhou
Knowledge-Based Systems 144, 65-76, 2018
2572018
Latest developments in gear defect diagnosis and prognosis: A review
A Kumar, CP Gandhi, Y Zhou, R Kumar, J Xiang
Measurement 158, 107735, 2020
2172020
FEM simulation-based generative adversarial networks to detect bearing faults
Y Gao, X Liu, J Xiang
IEEE Transactions on Industrial Informatics 16 (7), 4961-4971, 2020
2022020
Highly efficient fault diagnosis of rotating machinery under time-varying speeds using LSISMM and small infrared thermal images
X Li, H Shao, S Lu, J Xiang, B Cai
IEEE Transactions on Systems, Man, and Cybernetics: Systems 52 (12), 7328-7340, 2022
1802022
Fault diagnosis of angle grinders and electric impact drills using acoustic signals
A Glowacz, R Tadeusiewicz, S Legutko, W Caesarendra, M Irfan, H Liu, ...
Applied Acoustics 179, 108070, 2021
1802021
A data indicator-based deep belief networks to detect multiple faults in axial piston pumps
S Wang, J Xiang, Y Zhong, H Tang
Mechanical Systems and Signal Processing 112, 154-170, 2018
1782018
Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images
A Kumar, CP Gandhi, Y Zhou, R Kumar, J Xiang
Applied Acoustics 167, 107399, 2020
1672020
Novel convolutional neural network (NCNN) for the diagnosis of bearing defects in rotary machinery
A Kumar, G Vashishtha, CP Gandhi, Y Zhou, A Glowacz, J Xiang
IEEE Transactions on Instrumentation and Measurement 70, 1-10, 2021
1622021
Wavelet‐based detection of beam cracks using modal shape and frequency measurements
J Xiang, M Liang
Computer‐Aided Civil and Infrastructure Engineering 27 (6), 439-454, 2012
1532012
A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine
X Liu, H Huang, J Xiang
Knowledge-Based Systems 195, 105653, 2020
1402020
Crack detection in a shaft by combination of wavelet-based elements and genetic algorithm
J Xiang, Y Zhong, X Chen, Z He
International journal of solids and structures 45 (17), 4782-4795, 2008
1392008
Theoretical investigation of the contributions of the excitation forces to the vibration of an axial piston pump
S Ye, J Zhang, B Xu, S Zhu, J Xiang, H Tang
Mechanical Systems and Signal Processing 129, 201-217, 2019
1312019
Optimization of VMD using kernel-based mutual information for the extraction of weak features to detect bearing defects
A Kumar, Y Zhou, J Xiang
Measurement 168, 108402, 2021
1292021
Machinery fault diagnosis based on domain adaptation to bridge the gap between simulation and measured signals
Y Lou, A Kumar, J Xiang
IEEE Transactions on Instrumentation and Measurement 71, 1-9, 2022
1262022
Detect damages in conical shells using curvature mode shape and wavelet finite element method
J Xiang, T Matsumoto, Y Wang, Z Jiang
International Journal of Mechanical Sciences 66, 83-93, 2013
1242013
Rolling element bearing fault detection using PPCA and spectral kurtosis
J Xiang, Y Zhong, H Gao
Measurement 75, 180-191, 2015
1202015
The construction of 1D wavelet finite elements for structural analysis
JW Xiang, XF Chen, ZJ He, HB Dong
Computational Mechanics 40 (2), 325-339, 2007
1202007
Fault detection in gears using fault samples enlarged by a combination of numerical simulation and a generative adversarial network
Y Gao, X Liu, J Xiang
IEEE/ASME Transactions on Mechatronics 27 (5), 3798-3805, 2021
1192021
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