Jay Lee

30.5k citations
382 papers · 24.4k · 17 hit papers · h-index 68

Impact in

Papers in

Jay Lee

371 papers receiving 23.3k citations

Jay Lee's Hit Papers

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook 2020 · 432 citations
4320+6+13Years since publication10002.0k3.0k

Peers

Jay Lee
Comparison fields: 5 of 202
  • Industrial and Manufacturing Engineering 8.4k
  • Medical Laboratory Technology 1.1k
  • Safety, Risk, Reliability and Quality 3.4k
  • Control and Systems Engineering 8.3k
  • Management Information Systems 1.9k
Replace Lihui Wang with:
Lihui Wang China
Fei Tao China
Michael Pecht United States
Enrico Zio Italy
Andrew Kusiak United States
A.Y.C. Nee Singapore
Rommert Dekker Netherlands
Xun Xu New Zealand
Jiafu Wan China
Cengiz Kahraman Türkiye
Jay Lee relative to Lihui Wang China Lihui Wang's profile →
Citations per field
00.5×4.1×
Lihui Wang · 1×
Citations per year

Countries citing papers authored by Jay Lee

Since Specialization
Citations

This map shows the geographic impact of Jay Lee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jay Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay Lee more than expected).

Fields of papers citing papers by Jay Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jay Lee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jay Lee. The network helps show where Jay Lee may publish in the future.

Co-authors

The 25 scholars most cited alongside Jay Lee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jay Lee Line = papers co-authored together Jay Lee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 382 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Hit paper breakdown →
20143681
2
Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment
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20141472
3
Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications
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20131231
4
Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics
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20051217
5
Recent advances and trends in predictive manufacturing systems in big data environment
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2013866
6
A review on prognostics and health monitoring of Li-ion battery
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2011647
7
Industrial Artificial Intelligence for industry 4.0-based manufacturing systems
Hit paper breakdown →
2018624
8
Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility
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2014570
9
Intelligent prognostics tools and e-maintenance
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2006486
10
Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
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2020432
11
Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods
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2006416
12
A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems
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2008388
13
Robust performance degradation assessment methods for enhanced rolling element bearing prognostics
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2003374
14
Smart Agents in Industrial Cyber–Physical Systems
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2016369
15
Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation
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2015306
16 2004281
17
Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis
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2020280
18
Cyber-physical Systems Architecture for Self-Aware Machines in Industry 4.0 Environment
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2015264
19 2003248
20 2006239

About Jay Lee

Jay Lee is a scholar working on Control and Systems Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering, Safety, Risk, Reliability and Quality and Artificial Intelligence, having authored 382 papers that have together received 24.4k indexed citations. Recurring topics across this work include Machine Fault Diagnosis Techniques (96 papers), Fault Detection and Control Systems (79 papers), Industrial Vision Systems and Defect Detection (52 papers), Digital Transformation in Industry (39 papers), Manufacturing Process and Optimization (35 papers), Flexible and Reconfigurable Manufacturing Systems (35 papers), Reliability and Maintenance Optimization (34 papers) and Gear and Bearing Dynamics Analysis (27 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (8.4k citations), Medical Laboratory Technology (1.1k citations), Safety, Risk, Reliability and Quality (3.4k citations), Control and Systems Engineering (8.3k citations) and Management Information Systems (1.9k citations). Jay Lee has collaborated with scholars based in United States, China and India. Frequent co-authors include Hung-An Kao, Behrad Bagheri, Shanhu Yang, Hai Qiu, Jaskaran Singh, David Siegel, Jing Lin, Xiaodong Jia, Edzel Lapira and Jingliang Zhang. Their work appears in journals such as International Journal of Prognostics and Health Management, Mechanical Systems and Signal Processing, Manufacturing Letters, The International Journal of Advanced Manufacturing Technology and Computers in Industry.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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