Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions

T Zhang, J Chen, F Li, K Zhang, H Lv, S He, E Xu - ISA transactions, 2022 - Elsevier
The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …

A comprehensive review on convolutional neural network in machine fault diagnosis

J Jiao, M Zhao, J Lin, K Liang - Neurocomputing, 2020 - Elsevier
With the rapid development of manufacturing industry, machine fault diagnosis has become
increasingly significant to ensure safe equipment operation and production. Consequently …

Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning

Y Zhang, K Yu, Z Lei, J Ge, Y Xu, Z Li, Z Ren… - Expert Systems with …, 2023 - Elsevier
Offshore wind turbines play a vital role in transferring wind energy to electricity, which could
help relieve the energy crisis and improve the global climate. In general, offshore wind …

Federated learning for machinery fault diagnosis with dynamic validation and self-supervision

W Zhang, X Li, H Ma, Z Luo, X Li - Knowledge-Based Systems, 2021 - Elsevier
Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …

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

Z Zhao, T Li, J Wu, C Sun, S Wang, R Yan, X Chen - ISA transactions, 2020 - Elsevier
Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through
tremendous progress, which can help reduce costly breakdowns. However, different …

Review of tool condition monitoring in machining and opportunities for deep learning

G Serin, B Sener, AM Ozbayoglu, HO Unver - The International Journal of …, 2020 - Springer
Tool condition monitoring and machine tool diagnostics are performed using advanced
sensors and computational intelligence to predict and avoid adverse conditions for cutting …

Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review

D Neupane, J Seok - Ieee Access, 2020 - ieeexplore.ieee.org
A smart factory is a highly digitized and connected production facility that relies on smart
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …

Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method

J Li, Y Liu, Q Li - Measurement, 2022 - Elsevier
Data-driven intelligent method has been widely used in fault diagnostics. However, it is
observed that previous research studies focusing on imbalanced datasets for fault diagnosis …

Universal source-free domain adaptation method for cross-domain fault diagnosis of machines

Y Zhang, Z Ren, K Feng, K Yu, M Beer, Z Liu - Mechanical Systems and …, 2023 - Elsevier
Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge
from a labeled source domain to a new unlabeled target domain. Most existing methods …

A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning

K Yu, TR Lin, H Ma, X Li, X Li - Mechanical Systems and Signal Processing, 2021 - Elsevier
Limited condition monitoring data are recorded with label information in practice, which
make the fault identification task more challenging. A semi-supervised learning (SSL) …