World's Best Scientists 2026 revealed!

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Computer Science

D-Index
84
Citations
30561
World Ranking
847
National Ranking
125

Research.com Recognitions

  • 2019 - Fellow of the American Society of Mechanical Engineers

Overview

Ruqiang Yan is affiliated with Xi'an Jiaotong University in China, contributing extensively to the field of engineering with a focus on control and systems engineering. Their work spans multiple subfields including mechanical engineering, artificial intelligence, electrical and electronic engineering, and civil and structural engineering. The principal area of research covers machine fault diagnosis techniques alongside fault detection and control systems.

The scientist's recent publications examine advanced methodologies in fault diagnosis and prognostics within industrial scenarios. Notable papers include:

  • A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges (2021, Mechanical Systems and Signal Processing)
  • Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study (2020, ISA Transactions)
  • Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2020, IEEE Transactions on Industrial Electronics)
  • The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study (2021, Mechanical Systems and Signal Processing)
  • Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study (2021, IEEE Transactions on Instrumentation and Measurement)

The scientist frequently collaborates with several researchers, including Xuefeng Chen, Chuang Sun, Zhibin Zhao, Jiawen Xu, and Salvatore Baglio.

Their publication record spans a range of prominent venues related to instrumentation and measurement as well as mechanical systems and industrial informatics, such as:

  • IEEE Transactions on Instrumentation and Measurement
  • IEEE Instrumentation & Measurement Magazine
  • Mechanical Systems and Signal Processing
  • IEEE Transactions on Industrial Informatics
  • Journal of Manufacturing Systems

Research topics frequently addressed include:

  • Machine Fault Diagnosis Techniques
  • Fault Detection and Control Systems
  • Gear and Bearing Dynamics Analysis
  • Anomaly Detection Techniques and Applications
  • Structural Health Monitoring Techniques
  • Non-Destructive Testing Techniques
  • Industrial Vision Systems and Defect Detection

Ruqiang Yan has been recognized with the designation of Fellow of the American Society of Mechanical Engineers in 2019.

Best Publications

  • Deep learning and its applications to machine health monitoring

    Rui Zhao;Ruqiang Yan;Zhenghua Chen;Kezhi Mao

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

    Ruqiang Yan;Robert X. Gao;Xuefeng Chen

  • Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

    Siyu Shao;Stephen McAleer;Ruqiang Yan;Pierre Baldi

  • Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

    Rui Zhao;Dongzhe Wang;Ruqiang Yan;Kezhi Mao

  • Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.

    Rui Zhao;Ruqiang Yan;Jinjiang Wang;Kezhi Mao

  • A sparse auto-encoder-based deep neural network approach for induction motor faults classification

    Wenjun Sun;Siyu Shao;Rui Zhao;Ruqiang Yan;Ruqiang Yan

  • A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

    Weihua Li;Ruyi Huang;Jipu Li;Yixiao Liao

  • Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study.

    Zhibin Zhao;Tianfu Li;Jingyao Wu;Chuang Sun

  • 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

  • Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach

    Zhenghua Chen;Min Wu;Rui Zhao;Feri Guretno

  • Generative adversarial networks for data augmentation in machine fault diagnosis

    Siyu Shao;Pu Wang;Ruqiang Yan;Ruqiang Yan

  • Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing

    Chuang Sun;Meng Ma;Zhibin Zhao;Shaohua Tian

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

    Tianfu Li;Zhibin Zhao;Chuang Sun;Li Cheng

  • Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study

    Zhibin Zhao;Qiyang Zhang;Xiaolei Yu;Chuang Sun

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

    Ruqiang Yan;R.X. Gao

  • 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

  • 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

  • Multireceptive Field Graph Convolutional Networks for Machine Fault Diagnosis

    Tianfu Li;Zhibin Zhao;Chuang Sun;Ruqiang Yan

Frequent Co-Authors

Robert X. Gao
Robert X. Gao Case Western Reserve University
Kezhi Mao
Kezhi Mao Nanyang Technological University
Hongrui Cao
Hongrui Cao Xi'an Jiaotong University
Xiaoli Li
Xiaoli Li Singapore University of Technology and Design
Qingbo He
Qingbo He Shanghai Jiao Tong University
Lihui Wang
Lihui Wang Royal Institute of Technology
Bin He
Bin He Carnegie Mellon University
Reza Langari
Reza Langari Texas A&M University
Alessandra Flammini
Alessandra Flammini University of Brescia
Siliang Lu
Siliang Lu Anhui University

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