World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
73
Citations
25338
World Ranking
1567
National Ranking
817

Research.com Recognitions

  • 2006 - Fellow of the American Society of Mechanical Engineers

Overview

Robert X. Gao is affiliated with Case Western Reserve University in the United States and has a research focus primarily in the field of Engineering. Within this broad area, their work spans multiple subfields, including Industrial and Manufacturing Engineering, Control and Systems Engineering, Mechanical Engineering, Electrical and Electronic Engineering, and Biomedical Engineering.

The scientist's research topics reflect a concentration on manufacturing processes and systems. Key topics in their publications include:

  • Advanced machining processes and optimization
  • Industrial Vision Systems and Defect Detection
  • Manufacturing Process and Optimization
  • Machine Fault Diagnosis Techniques
  • Digital Transformation in Industry
  • Fault Detection and Control Systems
  • Advanced Machining and Optimization Techniques

Robert X. Gao has contributed extensively to several prominent publication venues. The most frequent venues are:

  • Journal of Manufacturing Systems
  • CIRP Annals
  • Manufacturing Letters
  • Procedia Manufacturing
  • Robotics and Computer-Integrated Manufacturing

Their notable recent papers include:

  • Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook, published in 2020 in the Journal of Manufacturing Science and Engineering
  • WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis, published in 2021 in IEEE Transactions on Systems Man and Cybernetics Systems
  • Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm, published in 2021 in the Journal of Manufacturing Systems
  • Physics guided neural network for machining tool wear prediction, published in 2020 in the Journal of Manufacturing Systems
  • Wavelet transform for rotary machine fault diagnosis:10 years revisited, published in 2023 in Mechanical Systems and Signal Processing

Frequent collaborators in their research include:

  • Jianjing Zhang
  • Lihui Wang
  • Clayton Cooper
  • Ihab Ragai
  • Xun Xu

Robert X. Gao was recognized as a Fellow of the American Society of Mechanical Engineers in 2006.

Best Publications

  • Deep learning and its applications to machine health monitoring

    Rui Zhao;Ruqiang Yan;Zhenghua Chen;Kezhi Mao

  • Deep learning for smart manufacturing: Methods and applications

    Jinjiang Wang;Yulin Ma;Laibin Zhang;Robert X. Gao

  • Wavelets for fault diagnosis of rotary machines: A review with applications

    Ruqiang Yan;Robert X. Gao;Xuefeng Chen

  • PCA-based feature selection scheme for machine defect classification

    A. Malhi;R.X. Gao

  • A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

    Dazhong Wu;Connor Jennings;Janis Terpenny;Robert X. Gao

  • Approximate Entropy as a diagnostic tool for machine health monitoring

    Ruqiang Yan;Robert X. Gao

  • Wavelets: Theory and Applications for Manufacturing

    Robert X. Gao;Ruqiang Yan

  • Symbiotic human-robot collaborative assembly

    L. Wang;R. Gao;J. Váncza;J. Váncza;J. Krüger;J. Krüger

  • WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

    Tianfu Li;Zhibin Zhao;Chuang Sun;Li Cheng

  • Digital Twin for rotating machinery fault diagnosis in smart manufacturing

    Jinjiang Wang;Lunkuan Ye;Robert X. Gao;Chen Li

  • Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

    Jorge F. Arinez;Qing Chang;Robert X. Gao;Chengying Xu

  • Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring

    Ruqiang Yan;R.X. Gao

  • Cloud-enabled prognosis for manufacturing

    Robert Gao;Lihui Wang;Roberto Teti;David Dornfeld

  • Long short-term memory for machine remaining life prediction

    Jianjing Zhang;Peng Wang;Ruqiang Yan;Robert X. Gao

  • Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines

    Ruqiang Yan;Yongbin Liu;Robert X. Gao

  • Machine learning-based image processing for on-line defect recognition in additive manufacturing

    Alessandra Caggiano;Jianjing Zhang;Vittorio Alfieri;Fabrizia Caiazzo

  • DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

    Siyu Shao;Ruqiang Yan;Yadong Lu;Peng Wang

  • Prognosis of Defect Propagation Based on Recurrent Neural Networks

    A Malhi;Ruqiang Yan;R X Gao

  • A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing

    Dazhong Wu;Shaopeng Liu;Li Zhang;Janis Terpenny

  • A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN

    Hui Wang;Jiawen Xu;Ruqiang Yan;Robert X. Gao

  • Performance enhancement of ensemble empirical mode decomposition

    Jian Zhang;Ruqiang Yan;Robert X. Gao;Zhihua Feng

Frequent Co-Authors

Ruqiang Yan
Ruqiang Yan Xi'an Jiaotong University
Lihui Wang
Lihui Wang Royal Institute of Technology
Jian Cao
Jian Cao Northwestern University
Dazhong Wu
Dazhong Wu University of Central Florida
Qingbo He
Qingbo He Shanghai Jiao Tong University
Soundar R. T. Kumara
Soundar R. T. Kumara Pennsylvania State University
Roberto Teti
Roberto Teti University of Naples Federico II
Thomas R. Kurfess
Thomas R. Kurfess Oak Ridge National Laboratory
József Váncza
József Váncza Budapest University of Technology and Economics
Xiangyu Wang
Xiangyu Wang Curtin University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Pursuing a computer science degree online has become increasingly popular thanks to the flexibility and diverse options available. Many students are seeking cheap online college classes to save on tuition while gaining a quality education. These affordable options make it easier for students from various backgrounds to access top programs.

If your academic record isn't perfect, don't worry. There are best colleges for low gpa that offer computer science degrees and support for students with lower GPAs. This inclusivity opens doors to rewarding tech careers for more learners.

For those looking to enter the tech workforce quickly, many universities offer computer science accelerated program options. These allow you to fast-track your studies and start your career sooner.

Additionally, computer science intersects with many fields, such as environmental science. This opens up unique job prospects. For example, learning what can you do with an environmental science degree can help you discover interdisciplinary career pathways that blend technology and sustainability.

Best Scientists Citing Robert X. Gao

Trending Scientists

Recently Published Articles