Jay Lee

@umd.edu

Mechanical Engineering
Univ. of Maryland College Park



                             

https://researchid.co/jay.lee

Dr. Jay Lee is Clark Distinguished Professor and Director of Industrial AI Center in the Mechanical Engineering Dept. of the Univ. of Maryland College Park. Previously, he served as an Ohio Eminent Scholar, L.W. Scott Alter Chair and Univ. Distinguished Professor at Univ. of Cincinnati. He was Founding Director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems ( during 2001-2019 with over 100 company memberships. IMS was selected as the most economically impactful I/UCRC in the NSF Economic Impact Study Report in 2012. He is also the Founding Director of Industrial AI Center ( ). He is a fellow of ASME, SME, PHM Society, and ISEAM, and a member of World Economic Forum (WEF) Global Future Council in Advanced Manufacturing and Value Chain. Previously served as Program Director of NSF during 1991-1998 and Director of United Technologies Research Center during 1998-2000.

RESEARCH, TEACHING, or OTHER INTERESTS

Mechanical Engineering, Industrial and Manufacturing Engineering, Engineering, Artificial Intelligence

47517

Scholar Citations

86

Scholar h-index

297

Scholar i10-index

Scopus Publications




RECENT SCHOLAR PUBLICATIONS

  • 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
    J Lee, H Su, M Macchi, A Polenghi, W Wu, Z Zhao, GQ Huang, K Allgood, ...
    Machine Learning: Engineering , 2026
    2026
  • Correction to: Case Studies in Digital Transformation
    A Crespo Márquez, T Seecharan, G Abdul-Nour, J Amadi-Echendu, J Lee
    Case Studies in Digital Transformation: Integration of Digital Technologies … , 2026
    2026
  • BioPrint-LKM: An evidence-grounded large knowledge model for bioprinting knowledge retrieval and parameter initialization
    X Huang, H Su, Z Cui, JM Lee, X Gao, R Hu, J Lee, WY Yeong
    International Journal of Bioprinting , 2026
    2026
  • V-TimesNet: Vision-Augmented TimesNet for Improved Anomaly Detection in Semiconductor Plasma Dry Etching
    R Wang, D Ji, C Liu, J Lee
    SSRN , 2025
    2025
    Citations: 4
  • Data issues in industrial AI systems: A meta-review and research strategy
    X Li, Y Cheng, C Møller, J Lee
    Computers in Industry 173, 104361 , 2025
    2025
    Citations: 13
  • UniFault: A Fault Diagnosis Foundation Model from Bearing Data
    E Eldele, M Ragab, X Qing, Edward, Z Chen, M Wu, X Li, J Lee
    arXiv:2504.01373 , 2025
    2025
    Citations: 13
  • Partial Domain Adaptation for Intelligent Machinery Fault Diagnosis: Leveraging Healthy-Only Target Data for Multi-Class Classification
    H Su, DY Ji, S Tsuruta, D Arimizu, Y Hachiya, K Wakimoto, J Lee
    Annual Conference of the PHM Society 17 (1) , 2025
    2025
    Citations: 1
  • Agentic AI for smart manufacturing
    J Lee, H Su
    Manufacturing Letters , 2025
    2025
    Citations: 11
  • Engineering artificial intelligence: framework, challenges, and future direction
    J Lee, H Su, DY Ji, T Minami
    Machine Learning: Engineering 1 (1), 013001 , 2025
    2025
    Citations: 13
  • Introduction to Industrial Artificial Intelligence
    DY Ji, H Su, T Minami, J Lee
    Advances in Artificial Intelligence Applications in Industrial and Systems … , 2025
    2025
    Citations: 1
  • Introducing machine learning: engineering
    J Lee
    Machine Learning: Engineering, Volume 1, Number 1 1 , 2025
    2025
  • Transfer learning and ensemble learning for fault diagnosis using vibration signals
    H Su, J Lee
    2025 ieee international conference on prognostics and health management … , 2025
    2025
    Citations: 1
  • Improving machine calibration performance through systematic feature design in semiconductor manufacturing
    DY Ji, M Sumiya, Y Kamaji, S Matsukura, W Li, J Lee
    2025 36th annual semi advanced semiconductor manufacturing conference (asmc … , 2025
    2025
    Citations: 1
  • Rethinking industrial artificial intelligence: A unified foundation framework
    J Lee, H Su
    arXiv preprint arXiv:2504.01797 , 2025
    2025
    Citations: 27
  • Novel topological machine learning methodology for stream-of-quality modeling in smart manufacturing
    J Lee, DY Ji, YM Hsu
    Manufacturing Letters 43, 60-63 , 2025
    2025
    Citations: 9
  • Multi-Class Gearbox Fault Diagnosis via Pre-Trained Model-based Domain Adaptation with Healthy-Only Target Data
    DY Ji, H Su, S Tsuruta, D Arimizu, Y Hachiya, K Wakimoto, J Lee
    PHM Society Asia-Pacific Conference 5 (1) , 2025
    2025
    Citations: 1
  • An advanced diagnostic model for gearbox degradation prediction under various operating conditions and degradation levels
    H Su, J Lee
    Annual Conference of the PHM Society 16 (1) , 2024
    2024
    Citations: 10
  • A novel technique for multiple failure modes classification based on deep forest algorithm
    J Taco, P Kundu, J Lee
    Journal of Intelligent Manufacturing 35 (7), 3115-3129 , 2024
    2024
    Citations: 9
  • A unified industrial large knowledge model framework in industry 4.0 and smart manufacturing
    J Lee, H Su
    International Journal of AI for Materials and Design 3681, 20 , 2024
    2024
  • PHM for Spacecraft Propulsion Systems: Developing Resilient Models for Real-World Challenges
    T Minami, DY Ji, J Lee
    PHM Society European Conference 8 (1), 7-7 , 2024
    2024
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • A cyber-physical systems architecture for industry 4.0-based manufacturing systems
    J Lee, B Bagheri, HA Kao
    Manufacturing letters 3, 18-23 , 2015
    2015
    Citations: 8142
  • 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
    2014
    Citations: 3245
  • 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
    2014
    Citations: 2099
  • 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
    2006
    Citations: 1870
  • 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
    2013
    Citations: 1786
  • Industrial Artificial Intelligence for industry 4.0-based manufacturing systems
    J Lee, H Davari, J Singh, V Pandhare
    Manufacturing letters 18, 20-23 , 2018
    2018
    Citations: 1280
  • 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
    2020
    Citations: 1044
  • A review on prognostics and health monitoring of Li-ion battery
    J Zhang, J Lee
    Journal of power sources 196 (15), 6007-6014 , 2011
    2011
    Citations: 981
  • Intelligent prognostics tools and e-maintenance
    J Lee, J Ni, D Djurdjanovic, H Qiu, H Liao
    Computers in industry 57 (6), 476-489 , 2006
    2006
    Citations: 915
  • 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
    2014
    Citations: 895
  • Handbook of maintenance management and engineering
    M Ben-Daya, SO Duffuaa, A Raouf, J Knezevic, D Ait-Kadi
    Springer London , 2009
    2009
    Citations: 732
  • 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
    2015
    Citations: 723
  • 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
    2007
    Citations: 712
  • 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
    2008
    Citations: 710
  • 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
    2016
    Citations: 623
  • 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
    2015
    Citations: 590
  • 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
    2003
    Citations: 558
  • 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
    2004
    Citations: 555
  • 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
    2003
    Citations: 472
  • 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
    2007
    Citations: 464

Publications

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