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Teng Wang, Yangjian Ji, Xiaojian Liu, Shuyou Zhang
Abstract
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A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
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Study of bearing currents in induction machine: diagnostic possibilities, fault detection, and prediction
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Bearing fault diagnosis under time-varying speed and load conditions via observer-based load torque analysis
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Bearing fault detection with vibration and acoustic signals: Comparison among different machine leaning classification methods
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A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions
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Recursive prototypical network with coordinate attention: a model for few-shot cross-condition bearing fault diagnosis
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Cross-domain meta learning fault diagnosis based on multi-scale dilated convolution and adaptive relation module
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Early-stage fault diagnosis of motor bearing based on kurtosis weighting and fusion of current-vibration signals
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An intelligent multi-local model bearing fault diagnosis method using small sample fusion
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Time-and frequency-domain fusion for source-free adaptation fault diagnosis
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10.1016/j.engappai.2026.114173 · 2026
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10.1109/lsp.2010.2075925 · doi-reference
Short-time fractional Fourier transform and its applications
10.1109/tsp.2009.2028095 · doi-reference
A novel meta-learning method based on relation network for train bearing fault diagnosis
10.1007/s11071-025-11555-9 · doi-reference
A novel lightweight relation network for cross-domain few-shot fault diagnosis
10.1016/j.measurement.2023.112697 · doi-reference
New statistical learning perspective for design of a physically interpretable prototypical neural network for machine condition monitoring
10.1016/j.ymssp.2022.110041 · doi-reference
Cross model fusion-based fault diagnosis for equipment considering multi-source signal fusion with small samples based on deep transfer learning
10.1016/j.measurement.2025.118203 · doi-reference
A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning
10.1016/j.neucom.2018.05.021 · doi-reference
Few-shot bearing fault diagnosis by semi-supervised meta-learning with graph convolutional neural network under variable working conditions
10.1016/j.measurement.2024.115402 · doi-reference
A novel lightweight DDPM-based data augmentation method for rotating machinery fault diagnosis with small sample
10.1016/j.ymssp.2025.112741 · doi-reference
A novel interpretable dynamic weighted domain adaptation network for cross-domain fault diagnosis of bearings under time-varying speeds
10.1016/j.engappai.2026.114173 · doi-reference
Time-and frequency-domain fusion for source-free adaptation fault diagnosis
10.1016/j.inffus.2024.102875 · doi-reference
An intelligent multi-local model bearing fault diagnosis method using small sample fusion
10.3390/s23177567 · doi-reference
10.1007/978-3-031-13841-6_13
10.1007/978-3-031-13841-6_13 · doi-reference
Early-stage fault diagnosis of motor bearing based on kurtosis weighting and fusion of current-vibration signals
10.3390/s24113373 · doi-reference
Cross-domain meta learning fault diagnosis based on multi-scale dilated convolution and adaptive relation module
10.1016/j.knosys.2022.110175 · doi-reference
Recursive prototypical network with coordinate attention: a model for few-shot cross-condition bearing fault diagnosis
10.1016/j.apacoust.2024.110442 · doi-reference
A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions
10.1016/j.ymssp.2021.108765 · doi-reference
Bearing fault detection with vibration and acoustic signals: Comparison among different machine leaning classification methods
10.1016/j.engfailanal.2022.106515 · doi-reference
Bearing fault diagnosis under time-varying speed and load conditions via observer-based load torque analysis
10.3390/en15103532 · doi-reference
Study of bearing currents in induction machine: diagnostic possibilities, fault detection, and prediction
10.1007/s00202-024-02411-x · doi-reference
A survey on fault diagnosis approaches for rolling bearings of railway vehicles
10.3390/pr10040724 · doi-reference
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
10.1007/s10462-022-10293-3 · doi-reference
A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
10.1016/j.asej.2022.101945 · doi-reference