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Konstantinos C. Gryllias
Konstantinos C. Gryllias
Associate Professor, Department of Mechanical Engineering, KULeuven, Belgium
在 kuleuven.be 的电子邮件经过验证
标题
引用次数
引用次数
年份
A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
W Li, R Huang, J Li, Y Liao, Z Chen, G He, R Yan, K Gryllias
Mechanical Systems and Signal Processing 167, 108487, 2022
4152022
A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks
Z Chen, A Mauricio, W Li, K Gryllias
Mechanical Systems and Signal Processing 140, 106683, 2020
3422020
Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine
Z Chen, K Gryllias, W Li
Mechanical Systems and Signal Processing 133, 2019
2932019
Rolling element bearing fault detection in industrial environments based on a K-means clustering approach
CT Yiakopoulos, KC Gryllias, IA Antoniadis
Expert Systems with Applications 38 (3), 2888-2911, 2011
2852011
A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments
KC Gryllias, IA Antoniadis
Engineering Applications of Artificial Intelligence 25 (2), 326-344, 2012
2572012
Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network
Z Chen, K Gryllias, W Li
IEEE Transactions on Industrial Informatics 16 (1), 339-349, 2019
2472019
Domain adversarial transfer network for cross-domain fault diagnosis of rotary machinery
Z Chen, G He, J Li, Y Liao, K Gryllias, W Li
IEEE Transactions on Instrumentation and Measurement 69 (11), 8702-8712, 2020
2012020
KDamping: A stiffness based vibration absorption concept
IA Antoniadis, SA Kanarachos, K Gryllias, IE Sapountzakis
Journal of Vibration and Control 24 (3), 588-606, 2018
1372018
A semi-supervised Support Vector Data Description-based fault detection method for rolling element bearings based on cyclic spectral analysis
C Liu, K Gryllias
Mechanical Systems and Signal Processing 140, 106682, 2020
1002020
Improved Envelope Spectrum via Feature Optimisation-gram (IESFOgram): A novel tool for rolling element bearing diagnostics under non-stationary operating conditions
A Mauricio, WA Smith, RB Randall, J Antoni, K Gryllias
Mechanical Systems and Signal Processing 144, 106891, 2020
972020
Estimation of the instantaneous rotation speed using complex shifted Morlet wavelets
KC Gryllias, IA Antoniadis
Mechanical Systems and Signal Processing 38 (1), 78-95, 2013
702013
Bearing diagnostics under strong electromagnetic interference based on Integrated Spectral Coherence
A Mauricio, J Qi, WA Smith, M Sarazin, RB Randall, K Janssens, ...
Mechanical systems and signal processing 140, 106673, 2020
652020
Cyclostationary-based Multiband Envelope Spectra Extraction for bearing diagnostics: The Combined Improved Envelope Spectrum
A Mauricio, K Gryllias
Mechanical Systems and Signal Processing 149, 2021
602021
A general anomaly detection framework for fleet-based condition monitoring of machines
K Hendrickx, W Meert, Y Mollet, J Gyselinck, B Cornelis, K Gryllias, ...
Mechanical Systems and Signal Processing 139, 106585, 2020
552020
Vibration based condition monitoring of wind turbine gearboxes based on cyclostationary analysis
A Mauricio, J Qi, K Gryllias
ASME 2018 Turbo Expo - Turbomachinery Technical Conference & Exposition …, 2018
54*2018
Simulation-driven domain adaptation for rolling element bearing fault diagnosis
C Liu, K Gryllias
IEEE Transactions on Industrial Informatics 18 (9), 5760-5770, 2021
502021
A discrepancy analysis methodology for rolling element bearing diagnostics under variable speed conditions
S Schmidt, PS Heyns, KC Gryllias
Mechanical Systems and Signal Processing 116, 40-61, 2019
502019
Cyclostationary modeling for local fault diagnosis of planetary gear vibration signals
RB Sun, ZB Yang, K Gryllias, XF Chen
Journal of Sound and Vibration 471, 115175, 2020
482020
An informative frequency band identification framework for gearbox fault diagnosis under time-varying operating conditions
S Schmidt, PS Heyns, KC Gryllias
Mechanical Systems and Signal Processing 158, 107771, 2021
402021
A peak energy criterion (pe) for the selection of resonance bands in complex shifted morlet wavelet (csmw) based demodulation of defective rolling element bearings vibration …
KC Gryllias, I Antoniadis
International Journal of Wavelets, Multiresolution and Information …, 2009
372009
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