Leaderboards/Gear and Bearing Dynamics Analysis

Gear and Bearing Dynamics Analysis

Last updated July 7, 2026

Rising Stars

New and hot

Rising Stars Researchers

1
Yifei Ding
Suzhou University of Science and Technology
2
You Keshun
University of South China
3
Hao Su
North China Electric Power University
4
Zhuang Ye
Huzhou Vocational and Technical College
5
Yiming Xiao
Hunan University
6
Farid K. Moghadam
Norwegian University of Science and Technology
7
Hongru Cao
Hunan University
8
Jingjie Luo
Hunan University
9
Linshan Jia
City University of Hong Kong
10
Jiang Zhao
Nanjing Forestry University

Rising Stars Papers

1
Meta-learning with elastic prototypical network for fault transfer diagnosis of bearings under unstable speeds
Reliability Engineering & System Safety|2024
197 cit.
2
Domain generalization for rotating machinery fault diagnosis: A survey
Advanced Engineering Informatics|2024
166 cit.
3
A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis
Reliability Engineering & System Safety|2024
124 cit.
4
Toward Efficient and Interpretative Rolling Bearing Fault Diagnosis via Quadratic Neural Network With Bi-LSTM
IEEE Internet of Things Journal|2024
118 cit.
5
Causal Disentanglement Domain Generalization for time-series signal fault diagnosis
Neural Networks|2024
102 cit.
6
Application of deep learning to fault diagnosis of rotating machineries
Measurement Science and Technology|2024
68 cit.
7
A performance-interpretable intelligent fusion of sound and vibration signals for bearing fault diagnosis via dynamic CAME
Nonlinear Dynamics|2024
58 cit.
8
A novel rolling bearing fault diagnosis method based on time-series fusion transformer with interpretability analysis
Nondestructive Testing And Evaluation|2024
44 cit.
9
Evaluating calibration of deep fault diagnostic models under distribution shift
Computers in Industry|2025
40 cit.
10
Domain-augmented meta ensemble learning for mechanical fault diagnosis from heterogeneous source domains to unseen target domains
Expert Systems with Applications|2024
33 cit.

Emerging Leaders

Gaining momentum

Emerging Leaders Researchers

1
Haidong Shao
South China University of Technology
2
Qing Ni
Northwestern Polytechnical University
3
Viktor Slavkovikj
Ghent University
4
Xingqiu Li
East China University of Science and Technology
5
Shuaipeng Wu
Shenzhen Institutes of Advanced Technology
6
Gaoliang Peng
Harbin Institute of Technology
7
Haizhou Chen
Qingdao University of Science and Technology
8
Deqiang He
Guangxi University
9
Yanfeng Han
Chongqing University
10
Xiang-Feng Gou
Tiangong University

Emerging Leaders Papers

1
Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics
Mechanical Systems and Signal Processing|2023
300 cit.
2
Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network
IEEE Transactions on Industrial Informatics|2022
227 cit.
3
A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains
Knowledge-Based Systems|2022
154 cit.
4
Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit
Reliability Engineering & System Safety|2023
149 cit.
5
Digital Twin Enabled Domain Adversarial Graph Networks for Bearing Fault Diagnosis
IEEE Transactions on Industrial Cyber-Physical Systems|2023
135 cit.
6
RTSMFFDE-HKRR: A fault diagnosis method for train bearing in noise environment
Measurement|2024
130 cit.
7
Prediction of bearing remaining useful life based on a two-stage updated digital twin
Advanced Engineering Informatics|2025
112 cit.
8
DCAGGCN: A novel method for remaining useful life prediction of bearings
Reliability Engineering & System Safety|2025
94 cit.
9
A digital twin methodology for vibration-based monitoring and prediction of gear wear
Wear|2025
92 cit.
10
AGFCN:A bearing fault diagnosis method for high-speed train bogie under complex working conditions
Reliability Engineering & System Safety|2025
91 cit.

Current Senior Leaders

Established & currently active

Current Senior Leaders Researchers

1
Ming J. Zuo
Fuzhou University
2
Ahmet Kahraman
The Ohio State University
3
Hongkai Jiang
Northwestern Polytechnical University
4
Hui Ma
Foshan University
5
Robert G. Parker
University of Utah
6
Fulei Chu
Tsinghua University
7
Yimin Shao
Chongqing University
8
Xuefeng Chen
Xi'an Jiaotong University
9
H. A. Spikes
Imperial College London
10
Bangchun Wen
Northeastern University

Current Senior Leaders Papers

1
The History and Mechanisms of ZDDP
Tribology Letters|2004
1.1k cit.
2
A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing
Mechanical Systems and Signal Processing|2004
834 cit.
3
Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection
Mechanical Systems and Signal Processing|2012
729 cit.
4
A sparse auto-encoder-based deep neural network approach for induction motor faults classification
Measurement|2016
692 cit.
5
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
ISA Transactions|2020
634 cit.
6
Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
Nature Communications|2024
607 cit.
7
Non-linear dynamics of a spur gear pair
Journal of Sound and Vibration|1990
605 cit.
8
Friction Modifier Additives
Tribology Letters|2015
587 cit.
9
Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing
IEEE Transactions on Industrial Informatics|2018
523 cit.
10
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
IEEE Transactions on Instrumentation and Measurement|2021
502 cit.

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