| 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 | 258 | 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 | 233 | 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 | 223 | 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 | 95 | 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 | 83 | 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 | 71 | 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 | 69* | 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 | 65 | 2020 |
| Industrial artificial intelligence J Lee, J Singh, M Azamfar arXiv preprint arXiv:1908.02150, 2019 | 58 | 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 | 32 | 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 | 30 | 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 | 12 | 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, 2763-2786, 2016 | 5 | 2016 |
| Refreshable Braille display device M Azamfar US Patent 10,276,066, 2019 | 4 | 2019 |
| Device and methods for continuosly refreshing a tactile display M Azamfar US Patent 10,269,264, 2019 | 3 | 2019 |
Jay LeeClark Distingusihed Professor and Director of Industrial AI, University of Maryland College ParkVerified email at umd.edu
