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Mechanical and Aerospace Engineering
USA
2026

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
101
Citations
54100
World Ranking
58
National Ranking
29

Research.com Recognitions

  • 2026 - Research.com Mechanical and Aerospace Engineering in United States Leader Award

Overview

Jay Lee is affiliated with the University of Maryland, College Park in the United States. Their research primarily spans the field of Engineering, with a significant focus on subfields such as Industrial and Manufacturing Engineering, Control and Systems Engineering, Mechanical Engineering, Electrical and Electronic Engineering, and Artificial Intelligence.

Their work covers a range of main topics including:

  • Machine Fault Diagnosis Techniques
  • Industrial Vision Systems and Defect Detection
  • Digital Transformation in Industry
  • Fault Detection and Control Systems
  • Manufacturing Process and Optimization
  • Anomaly Detection Techniques and Applications
  • Flexible and Reconfigurable Manufacturing Systems

Jay Lee has contributed to various academic venues with multiple publications. The frequent publication venues include:

  • International Journal of Prognostics and Health Management
  • Manufacturing Letters
  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Annual Conference of the PHM Society

Notable recent papers authored by Jay Lee are:

  • Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing, 2020, IET Collaborative Intelligent Manufacturing
  • Intelligent Maintenance Systems and Predictive Manufacturing, 2020, Journal of Manufacturing Science and Engineering

Other recent impactful works related to the field but authored by different researchers include:

  • Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook, 2020, IEEE Access
  • Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis, 2020, Mechanical Systems and Signal Processing
  • Field-synchronized Digital Twin framework for production scheduling with uncertainty, 2020, Journal of Intelligent Manufacturing

Jay Lee has frequently collaborated with several researchers, including:

  • Xiang Li
  • Xiaodong Jia
  • Jianshe Feng
  • Qibo Yang
  • Wenzhe Li

In addition to journal articles, Jay Lee has published book content with Frontiers Media, including the 2023 work titled Hydrology, Water Resources, Sustainable Development.

Best Publications

  • A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems

    Jay Lee;Behrad Bagheri;Hung-An Kao

  • Model predictive control: past, present and future

    Manfred Morari;Jay H. Lee

  • Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment

    Jay Lee;Hung An Kao;Shanhu Yang

  • Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

    Jay Lee;Fangji Wu;Wenyu Zhao;Masoud Ghaffari

  • Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

    Hai Qiu;Jay Lee;Jing Lin;Gang Yu

  • Recent advances and trends in predictive manufacturing systems in big data environment

    Jay Lee;Edzel Lapira;Behrad Bagheri;Hung-an Kao

  • Intelligent prognostics tools and e-maintenance

    Jay Lee;Jun Ni;Dragan Djurdjanovic;Hai Qiu

  • A review on prognostics and health monitoring of Li-ion battery

    Jingliang Zhang;Jay Lee

  • Statistical Analysis with ArcView GIS

    Jay Lee;David Wing-Shun Wong

  • Industrial Artificial Intelligence for industry 4.0-based manufacturing systems

    Jay Lee;Hossein Davari;Jaskaran Singh;Vibhor Pandhare

  • Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility

    Seyed Mohammad Rezvanizaniani;Zongchang Liu;Yan Chen;Jay Lee

  • Model predictive control: Review of the three decades of development

    Jay Hyung Lee

  • Brief Constrained linear state estimation-a moving horizon approach

    Christopher V. Rao;James B. Rawlings;Jay H. Lee

  • Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods

    Runqing Huang;Lifeng Xi;Xinglin Li;C. Richard Liu

  • Cellulose crystallinity--a key predictor of the enzymatic hydrolysis rate

    Mélanie Hall;Prabuddha Bansal;Jay H. Lee;Matthew J. Realff

  • Genome sequence of mungbean and insights into evolution within Vigna species

    Yang Jae Kang;Sue K. Kim;Moon Young Kim;Puji Lestari

  • A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems

    Tianyi Wang;Jianbo Yu;D. Siegel;J. Lee

  • Model-based iterative learning control with a quadratic criterion for time-varying linear systems

    Jay H. Lee;Kwang S. Lee;Won C. Kim

  • Robust performance degradation assessment methods for enhanced rolling element bearing prognostics

    Hai Qiu;Jay Lee;Jing Lin;Gang Yu

  • Maintenance: Changing role in life cycle management

    Shozo Takata;F. Kimura;F. J A M Van Houten;E. Westkämper

  • Worst-case formulations of model predictive control for systems with bounded parameters

    J. H. Lee;Zhenghong Yu

Frequent Co-Authors

Xinyue Ye
Xinyue Ye Texas A&M University
Lifeng Xi
Lifeng Xi Shanghai Jiao Tong University
Ming Zhao
Ming Zhao Xi'an Jiaotong University
Muammer Koç
Muammer Koç Hamad bin Khalifa University
Pradeep Lall
Pradeep Lall Auburn University
Chengliang Liu
Chengliang Liu Shanghai Jiao Tong University
Wenze Yue
Wenze Yue Zhejiang University
Armando Walter Colombo
Armando Walter Colombo University of Applied Sciences Emden Leer
Jianbo Yu
Jianbo Yu Tongji University
Paulo Leitão
Paulo Leitão Polytechnic Institute of Bragança

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