Moslem Azamfar

University of Cincinnati — United States

Engineering · Physical Sciences

13h-index1.5kcitations17works0.02yr avg
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Research Topics

Digital Transformation in Industry(6), Machine Fault Diagnosis Techniques(5), Fault Detection and Control Systems(5), Flexible and Reconfigurable Manufacturing Systems(4), Industrial Vision Systems and Defect Detection(4)

Publications17 total

A unified digital twin framework for shop floor design in industry 4.0 manufacturing systems
Manufacturing Letters·2021· 52 cited
Intelligent Maintenance Systems and Predictive Manufacturing
Journal of Manufacturing Science and Engineering·2020· 202 cited
Cross-domain gearbox diagnostics under variable working conditions with deep convolutional transfer learning
Journal of Vibration and Control·2020· 35 cited
Deep Learning-Based Domain Adaptation Method for Fault Diagnosis in Semiconductor Manufacturing
IEEE Transactions on Semiconductor Manufacturing·2020· 73 cited
Intelligent ball screw fault diagnosis using a deep domain adaptation methodology
Mechanism and Machine Theory·2020· 106 cited
Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis
Mechanical Systems and Signal Processing·2020· 321 cited
Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing
IET Collaborative Intelligent Manufacturing·2020· 232 citedOpen Access
A systematic review of machine learning algorithms for prognostics and health management of rolling element bearings: fundamentals, concepts and applications
Measurement Science and Technology·2020· 78 cited
Industrial AI and predictive analytics for smart manufacturing systems
Smart Manufacturing·2020· 33 cited
Contributors
Smart Manufacturing·2020
Deep learning-based cross-domain adaptation for gearbox fault diagnosis under variable speed conditions
Measurement Science and Technology·2019· 78 cited
Industrial Artificial Intelligence
arXiv (Cornell University)·2019· 13 citedOpen Access
Industrial AI:is it manufacturing’s guiding light?
2019· 4 cited
A blockchain enabled Cyber-Physical System architecture for Industry 4.0 manufacturing systems
Manufacturing Letters·2019· 256 cited
Detection and diagnosis of bottle capping failures based on motor current signature analysis
Procedia Manufacturing·2019· 14 citedOpen Access
Simple Formulae for Control of Industrial Time Delay Systems
Latin American Journal of Solids and Structures·2016· 3 citedOpen Access
Development of the tuning formula for unstable first order processes with time delay
2014

Frequent Co-authors

Jay Lee(14), Jaskaran Singh(11), Xiang Li(3), Vibhor Pandhare(3), Iñaki Bravo-Imaz(1), Shahin Siahpour(1), Jun Ni(1), Baoyang Jiang(1), Jianshe Feng(1), Fei Li(1), Abhijeet Ainapure(1), Behrad Bagheri(1), Xiaodong Jia(1), Hoseein Davari(1), J.-M. Jee(1), Amir H.D. Markazi(1), Aryan Amiri(1), Danial Aghapour Olilo(1), Majid Moradi Aliabadi(1), J.J. Anaya(1)