| A blockchain enabled Cyber-Physical System architecture for Industry 4.0 manufacturing systems J Lee, M Azamfar, J Singh Manufacturing letters 20, 34-39, 2019 | 270 | 2019 |
| Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis M Azamfar, J Singh, I Bravo-Imaz, J Lee Mechanical Systems and Signal Processing 144, 106861, 2020 | 259 | 2020 |
| Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing J Lee, M Azamfar, J Singh, S Siahpour IET Collaborative Intelligent Manufacturing 2 (1), 34-36, 2020 | 236 | 2020 |
| Intelligent maintenance systems and predictive manufacturing J Lee, J Ni, J Singh, B Jiang, M Azamfar, J Feng Journal of Manufacturing Science and Engineering 142 (11), 110805, 2020 | 115 | 2020 |
| Intelligent ball screw fault diagnosis using a deep domain adaptation methodology M Azamfar, X Li, J Lee Mechanism and Machine Theory 151, 103932, 2020 | 90 | 2020 |
| Deep learning-based cross-domain adaptation for gearbox fault diagnosis under variable speed conditions J Singh, M Azamfar, A Ainapure, J Lee Measurement Science and Technology 31 (5), 055601, 2020 | 76 | 2020 |
| A systematic review of machine learning algorithms for PHM of rolling element bearings: fundamentals, concepts, and applications J Singh, M Azamfar, F Li, J Lee Measurement Science and Technology 12001, 2020 | 75* | 2020 |
| Deep learning-based domain adaptation method for fault diagnosis in semiconductor manufacturing M Azamfar, X Li, J Lee IEEE Transactions on Semiconductor Manufacturing 33 (3), 445-453, 2020 | 69 | 2020 |
| Industrial artificial intelligence J Lee, J Singh, M Azamfar arXiv preprint arXiv:1908.02150, 2019 | 63 | 2019 |
| A unified digital twin framework for shop floor design in industry 4.0 manufacturing systems J Lee, M Azamfar, B Bagheri Manufacturing Letters 27, 87-91, 2021 | 34 | 2021 |
| Cross-domain gearbox diagnostics under variable working conditions with deep convolutional transfer learning M Azamfar, J Singh, X Li, J Lee Journal of Vibration and Control 27 (7-8), 854-864, 2021 | 33 | 2021 |
| Industrial AI and predictive analytics for smart manufacturing systems J Lee, J Singh, M Azamfar, V Pandhare Smart Manufacturing: Concepts and Methods, 213-244, 2020 | 27 | 2020 |
| Detection and diagnosis of bottle capping failures based on motor current signature analysis M Azamfar, X Jia, V Pandhare, J Singh, H Davari, J Lee Procedia Manufacturing 34, 840-846, 2019 | 14 | 2019 |
| Moshrefifar and Azamfar method, a new cycle counting method for evaluating fatigue life M Azamfar, M Moshrefifar International journal of fatigue 69, 2-15, 2014 | 13 | 2014 |
| Industrial AI: is it manufacturing’s guiding light L Jay, S Jaskaran, M Azamfar Manuf Leadersh Counc, 26-36, 2019 | 11 | 2019 |
| Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature A Moslem, S Jaskaran, B Inaki, L Jay Mech. Syst. Signal Pr 144, 1-18, 2020 | 9 | 2020 |
| Industrial AI: a systematic framework for AI in industrial applications LEE Jay, S Jaskaran, A Moslem, K SUN China Mechanical Engineering 31 (1), 37-48, 2020 | 6 | 2020 |
| Simple formulae for control of industrial time delay systems M Azamfar, AHD Markazi Latin American Journal of Solids and Structures 13 (14), 2763-2786, 2016 | 5 | 2016 |
| Refreshable Braille display device M Azamfar US Patent 10,276,066, 2019 | 4 | 2019 |
| Manufacturing Letters J Lee, M Azamfar, J Singh January, 2015 | 4 | 2015 |
Jay LeeClark Distingusihed Professor and Director of Industrial AI, University of Maryland College Park在 umd.edu 的电子邮件经过验证
Jay LeeClark Distingusihed Professor and Director of Industrial AI, University of Maryland College Park在 umd.edu 的电子邮件经过验证
Jaskaran SinghThapar Institute of Engineering and Technology在 thapar.edu 的电子邮件经过验证