Leaderboards/Machine Fault Diagnosis Techniques

Machine Fault Diagnosis Techniques

Last updated July 7, 2026

Rising Stars

New and hot

Rising Stars Researchers

1
Rosena Shintabella
Universitas Wiralodra
2
You Keshun
University of South China
3
Zhuang Ye
Huzhou Vocational and Technical College
4
Lucas Costa Brito
Universidade Federal de Uberlândia
5
Linshan Jia
City University of Hong Kong
6
Daoquan Chen
Zhejiang Institute of Mechanical and Electrical Engineering
7
Przemysław Pietrzak
Wrocław University of Science and Technology
8
Mo Yu
Tsinghua University
9
Jinghui Tian
Yanshan University
10
Tauheed Mian
Catholic University College of Bruges–Ostend

Rising Stars Papers

1
Loss of Life Transformer Prediction Based on Stacking Ensemble Improved by Genetic Algorithm By IJISRT
International Journal of Innovative Science and Research Technology (IJISRT)|2024
1.3k cit.
2
A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis
Reliability Engineering & System Safety|2024
124 cit.
3
Toward Efficient and Interpretative Rolling Bearing Fault Diagnosis via Quadratic Neural Network With Bi-LSTM
IEEE Internet of Things Journal|2024
118 cit.
4
Causal Disentanglement Domain Generalization for time-series signal fault diagnosis
Neural Networks|2024
102 cit.
5
AttMoE: Attention with Mixture of Experts for remaining useful life prediction of lithium-ion batteries
Journal of Energy Storage|2024
81 cit.
6
A performance-interpretable intelligent fusion of sound and vibration signals for bearing fault diagnosis via dynamic CAME
Nonlinear Dynamics|2024
58 cit.
7
Applications of machine learning in phylogenetics
Molecular Phylogenetics and Evolution|2024
48 cit.
8
A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data
Neural Networks|2024
45 cit.
9
A novel rolling bearing fault diagnosis method based on time-series fusion transformer with interpretability analysis
Nondestructive Testing And Evaluation|2024
44 cit.
10
DTMPI-DIVR: A digital twins for multi-margin physical information via dynamic interaction of virtual and real sound-vibration signals for bearing fault diagnosis without real fault samples
Expert Systems with Applications|2025
28 cit.

Emerging Leaders

Gaining momentum

Emerging Leaders Researchers

1
Haidong Shao
South China University of Technology
2
Yuyan Zhang
Lanzhou University
3
Zitong Zhou
Nanjing University of Posts and Telecommunications
4
Naipeng Li
Chinese Academy of Sciences
5
Olga Fink
International Institute for Management Development
6
Symone Gomes Soares Alcalá
Universidade Federal de Goiás
7
Adi Wibowo
Diponegoro University
8
Xingqiu Li
East China University of Science and Technology
9
Viktor Slavkovikj
Ghent University
10
Qing Ni
Northwestern Polytechnical University

Emerging Leaders Papers

1
Loss of Life Transformer Prediction Based on Stacking Ensemble Improved by Genetic Algorithm By IJISRT
International Journal of Innovative Science and Research Technology (IJISRT)|2024
1.3k cit.
2
Fusing physics-based and deep learning models for prognostics
Repository for Publications and Research Data (ETH Zurich)|2022
349 cit.
3
Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics
Mechanical Systems and Signal Processing|2023
300 cit.
4
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.
5
A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains
Knowledge-Based Systems|2022
154 cit.
6
Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit
Reliability Engineering & System Safety|2023
149 cit.
7
Digital Twin Enabled Domain Adversarial Graph Networks for Bearing Fault Diagnosis
IEEE Transactions on Industrial Cyber-Physical Systems|2023
135 cit.
8
Prior Knowledge-Augmented Self-Supervised Feature Learning for Few-Shot Intelligent Fault Diagnosis of Machines
IEEE Transactions on Industrial Electronics|2022
125 cit.
9
Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series
Proceedings of the National Academy of Sciences|2022
118 cit.
10
Targeted transfer learning through distribution barycenter medium for intelligent fault diagnosis of machines with data decentralization
Expert Systems with Applications|2023
111 cit.

Current Senior Leaders

Established & currently active

Current Senior Leaders Researchers

1
Norden E. Huang
National Central University
2
Xuefeng Chen
Xi'an Jiaotong University
3
Zhifeng Zhang
Ningxia Normal University
4
Dominique Zosso
Montana State University
5
Yaguo Lei
Xi'an Jiaotong University
6
Hongkai Jiang
Northwestern Polytechnical University
7
Ming J. Zuo
Fuzhou University
8
Robert X. Gao
Case Western Reserve University
9
Michael Pecht
Life Cycle Engineering (United States)
10
Steven X. Ding
University of Duisburg-Essen

Current Senior Leaders Papers

1
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences|1998
23.3k cit.
2
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing|1993
9.1k cit.
3
Variational Mode Decomposition
IEEE Transactions on Signal Processing|2014
8.6k cit.
4
ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD
Advances in Adaptive Data Analysis|2008
8.6k cit.
5
A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches
IEEE Transactions on Industrial Electronics|2015
3k cit.
6
Deep learning and its applications to machine health monitoring
Mechanical Systems and Signal Processing|2018
2.6k cit.
7
A NEW VIEW OF NONLINEAR WATER WAVES: The Hilbert Spectrum
Annual Review of Fluid Mechanics|1999
2.1k cit.
8
A study of the characteristics of white noise using the empirical mode decomposition method
Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences|2004
1.7k cit.
9
COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOVEL NOISE ENHANCED DATA ANALYSIS METHOD
Advances in Adaptive Data Analysis|2010
1.5k cit.
10
Deep Residual Shrinkage Networks for Fault Diagnosis
IEEE Transactions on Industrial Informatics|2019
1.4k cit.

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