Jay Lee
Jay Lee
Clark Distingusihed Professor and Director of Industrial AI, University of Maryland College Park
Verified email at - Homepage
Cited by
Cited by
A cyber-physical systems architecture for industry 4.0-based manufacturing systems
J Lee, B Bagheri, HA Kao
Manufacturing letters 3, 18-23, 2015
Service innovation and smart analytics for industry 4.0 and big data environment
J Lee, HA Kao, S Yang
Procedia cirp 16, 3-8, 2014
Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications
J Lee, F Wu, W Zhao, M Ghaffari, L Liao, D Siegel
Mechanical systems and signal processing 42 (1-2), 314-334, 2014
Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics
H Qiu, J Lee, J Lin, G Yu
Journal of sound and vibration 289 (4-5), 1066-1090, 2006
Recent advances and trends in predictive manufacturing systems in big data environment
J Lee, E Lapira, B Bagheri, H Kao
Manufacturing letters 1 (1), 38-41, 2013
Intelligent prognostics tools and e-maintenance
J Lee, J Ni, D Djurdjanovic, H Qiu, H Liao
Computers in industry 57 (6), 476-489, 2006
A review on prognostics and health monitoring of Li-ion battery
J Zhang, J Lee
Journal of power sources 196 (15), 6007-6014, 2011
Industrial Artificial Intelligence for industry 4.0-based manufacturing systems
J Lee, H Davari, J Singh, V Pandhare
Manufacturing letters 18, 20-23, 2018
Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility
SM Rezvanizaniani, Z Liu, Y Chen, J Lee
Journal of power sources 256, 110-124, 2014
Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods
R Huang, L Xi, X Li, CR Liu, H Qiu, J Lee
Mechanical systems and signal processing 21 (1), 193-207, 2007
A similarity-based prognostics approach for remaining useful life estimation of engineered systems
T Wang, J Yu, D Siegel, J Lee
2008 international conference on prognostics and health management, 1-6, 2008
Industrial big data analytics and cyber-physical systems for future maintenance & service innovation
J Lee, HD Ardakani, S Yang, B Bagheri
Procedia cirp 38, 3-7, 2015
Handbook of maintenance management and engineering
M Ben-Daya, SO Duffuaa, A Raouf, J Knezevic, D Ait-Kadi
Springer London, 2009
Robust performance degradation assessment methods for enhanced rolling element bearing prognostics
H Qiu, J Lee, J Lin, G Yu
Advanced Engineering Informatics 17 (3-4), 127-140, 2003
Maintenance: changing role in life cycle management
S Takata, F Kirnura, FJAM van Houten, E Westkamper, M Shpitalni, ...
CIRP annals 53 (2), 643-655, 2004
Smart agents in industrial cyber–physical systems
P Leitao, S Karnouskos, L Ribeiro, J Lee, T Strasser, AW Colombo
Proceedings of the IEEE 104 (5), 1086-1101, 2016
Cyber-physical systems architecture for self-aware machines in industry 4.0 environment
B Bagheri, S Yang, HA Kao, J Lee
IFAC-PapersOnLine 48 (3), 1622-1627, 2015
Watchdog Agent—an infotronics-based prognostics approach for product performance degradation assessment and prediction
D Djurdjanovic, J Lee, J Ni
Advanced Engineering Informatics 17 (3-4), 109-125, 2003
Reliability-centered predictive maintenance scheduling for a continuously monitored system subject to degradation
X Zhou, L Xi, J Lee
Reliability engineering & system safety 92 (4), 530-534, 2007
Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook
RS Peres, X Jia, J Lee, K Sun, AW Colombo, J Barata
IEEE access 8, 220121-220139, 2020
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