JEMA. On recommended interval of updating IMs. 2000.
Jiang K, Yang Z, Jin T, Chen C, Liu Z, Zhang B. CNN-based rolling bearing fault diagnosis method with quantifiable interpretability. IEEE Trans Instrum Meas. 2025.
Zhu Z, Peng G, Chen Y, Gao H. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis. Neurocomputing. 2019;323:62–75.
Wu X, Wen C, Wang Z, Liu W, Yang J. A novel ensemble-learning-based convolution neural network for handling imbalanced data. Cogn Comput. 2024;16(1):177–90.
Keshun Y, Puzhou W, Yingkui G. Towards efficient and interpretative rolling bearing fault diagnosis via quadratic neural network With Bi-LSTM. IEEE Internet Things J. 2024.
Mohammad-Alikhani A, Nahid-Mobarakeh B, Hsieh M-F. One-dimensional LSTMregulated deep residual network for data-driven fault detection in electric machines. IEEE Trans Ind Electron. 2023.
Keshun Y, Puzhou W, Peng H, Yingkui G. A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis. Reliab Eng Syst Saf. 2025;253:110556.
Wang M, Yu J, Leng H, Du X, Liu Y. Bearing fault detection by using graph autoencoder and ensemble learning. Sci Rep. 2024;14(1):5206.
Hu J, Zhang Y, Li W, Zheng X, Tian Z. Trustworthy artificial intelligence based on an explicable temporal feature network for industrial fault diagnosis. Cogn Comput. 2024;16(2):534–45.
Lin J, Shao H, Zhou X, Cai B, Liu B. Generalized MAML for few-shot crossdomain fault diagnosis of bearing driven by heterogeneous signals. Expert Syst Appl. 2023;230:120696.
Hoang D-T, Kang H-J. A survey on deep learning based bearing fault diagnosis. Neurocomputing. 2019;335:327–35.
Liu H, Yan S, Huang M, Huang Z. A fault diagnosis method for hydraulic system based on multi-branch neural networks. Eng Appl Artif Intell. 2024;137:109188.
Zhang Z, Wu N, Gong L, Luan R, Cao J, Zhang C. An ultrahigh power density and ultralow wear GaN-based tribovoltaic nanogenerator for sliding ball bearing as self- powered wireless sensor node. Adv Mater. 2024;36(6):2310098.
Irfan M, Khan NA, Mushtaq Z, Kareri T, Faraj Mursal SN, Shaheen AUR, et al. A computationally efficient method for induction motor bearing fault detection based on parallel convolutions and semi-supervised GAN. Nondestruct Test Eval. 2024;1–27.
Xu X, Chen X, Zhao Y. Data imbalance bearing fault diagnosis based on fusion attention mechanism and global feature cross GAN network. Meas Sci Technol. 2024;35(10):106136.
Wei Y, Xiao Z, Chen X, Gu X, Schröder K-U. A bearing fault data augmentation method based on hybrid-diversity loss diffusion model and parameter transfer. Reliab Eng Syst Saf. 2025;253:110567.
Shao S, McAleer S, Yan R, Baldi P. Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans Ind Inform. 2018;15(4):2446–55.
Zhang A, Li S, Cui Y, Yang W, Dong R, Hu J. Limited data rolling bearing fault diagnosis with few-shot learning. IEEE Access. 2019;7:110895–904.
Zhao X, Ma M, Shao F. Bearing fault diagnosis method based on improved Siamese neural network with small sample. J Cloud Comput. 2022;11(1):1–17.
Wen C, Xue Y, Liu W, Chen G, Liu X. Bearing fault diagnosis via fusing small samples and training multi-state Siamese neural networks. Neurocomputing. 2024;576:127355.
Jiang C, Chen H, Xu Q, Wang X. Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks. J Intell Manuf. 2023;34(4):1667–81.
Tang T, Qiu C, Yang T, Wang J, Zhao J, Chen M, et al. A novel lightweight relation network for cross-domain few-shot fault diagnosis. Measurement. 2023;213:112697.
Shao H, Zhou X, Lin J, Liu B. Few-shot cross-domain fault diagnosis of bearing driven by task-supervised ANIL. IEEE Internet Things J. 2024;11(13):22892–902.
Zhou W, Ma C, Yang L, Luo F, Liu J. Regulation of thermo-fluid-solid coupling characteristics in high-speed spindle-bearing system for boring machine tool based on sinteredcore heat pipes. Int Commun Heat Mass Transfer. 2024;157:107717.
Vinyals O, Blundell C, Lillicrap T, Wierstra D, et al. Matching networks for one shot learning. Adv Neural Inf Process. 2016;29.
Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning. Adv Neural Inf Process Syst. 2017;30.
Huang Q, Li C, Han Y, Shang J, Zhang Y. Semi-supervised prototype networks with similarity information selection for fault diagnosis of wind turbine gearboxes. IEEE Trans Instrum Meas. 2025.
Śpiewak S. Design solution for an instrument to determine the load bearing capacity of hexagonal gabion meshed nets using a conventional strength testing machine. Eng Struct. 2024;311:118197.
Dong X, Zhang C, Liu H, Wang D, Chen Y, Wang T. A new cross-domain bearing fault diagnosis method with few samples under different working conditions. J Manuf Process. 2025;135:359–74.
Hou R, Chang H, Ma B, Shan S, Chen X. Cross attention network for few-shot classification. Adv Neural Inf Process Syst. 2019;32.
Rahman AU, Alsenani Y, Zafar A, Ullah K, Rabie K, Shongwe T. Enhancing heart disease prediction using a self-attention-based transformer model. Sci Rep. 2024;14(1):514.
Wang S, Shi J, Yang W, Yin Q. High and low frequency wind power prediction based on Transformer and BiGRU-Attention. Energy. 2024;288:129753.
EskandariNasab M, Raeisi Z, Lashaki RA, Najafi H. A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis. Sci Rep. 2024;14(1):8861.
Karmakar P, Teng SW, Lu G. Thank you for attention: a survey on attentionbased artificial neural networks for automatic speech recognition. Intell Syst Appl. 2024;200406.
Ge Q, Li J, Wang X, Deng Y, Zhang K, Sun H. LiteTransNet: an interpretable approach for landslide displacement prediction using transformer model with attention mechanism. Eng Geol. 2024;331:107446.
Lee S, Choi J, Kim HJ. Multi-criteria token fusion with one-step-ahead attention for efficient vision transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024. pp. 15741–15750.
Han D, Ye T, Han Y, Xia Z, Pan S, Wan P, Song S, Huang G. Agent attention: on the integration of softmax and linear attention. In: European conference on computer vision. Springer; 2024. pp. 124–140.
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. pp. 7132–7141.
Woo S, Park J, Lee J-Y, Kweon IS. Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). 2018. pp. 3–19.
Park J, Woo S, Lee J-Y, Kweon IS. Bam: bottleneck attention module. 2018. arXiv:1807.06514
Li C, Wang Y, Fang Z, Li P. Hyperspectral image classification based on multi- branch adaptive feature fusion network. IEEE Trans Geosci Remote Sens. 2024.
Liu X, Ng AH-M, Ge L, Lei F, Liao X. Multi-branch fusion: a multi-branch attention framework by combining graph convolutional network and CNN for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2024.
Li Z, Wang T, Mei C, Pei Z. Multi-branch U-net for interactive segmentation. IEEE Signal Process Lett. 2024.
Vu M-H, Tran N-D, Le H-M-Q, Tran T-T, Pham V-T. MCST-Net: a multi- cross-spatial attention U-net with transformer block for skin lesion segmentation. In: Conference on information technology and its applications. Springer. 2024, pp. 397–408.
Nguyen V-Q, Nguyen Q-H, Tran T-T. ConvmixFormer-Unet: a new approach for medical image segmentation based on convmixer and transformer. In: 2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE; 2023. pp. 662–667.
Dong Y, Liu Y, Li X. MRMNet: multi-scale residual multi-branch neural network for object detection. Neurocomputing. 2024;596:127886.
Yang Z, Guan Q, Zhao K, Yang J, Xu X, Long H, Tang Y. Multi-branch auxiliary fusion YOLO with Re-parameterization heterogeneous convolutional for accurate object detection. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer; 2024. pp. 492–505.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. pp. 1–9.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. pp. 2818–2826.
Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence. Vol. 31. 1. 2017.
Jin Y, Qin C, Zhang Z, Tao J, Liu C. A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions. Sci China Technol Sci. 2022;65(11):2551–63.
Zhang W, Peng G, Li C, Chen Y, Zhang Z. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors. 2017;17(2):425.
Zhang W, Zhang F, Chen W, Jiang Y, Song D. Fault state recognition of rolling bearing based fully convolutional network. Comput Sci Eng. 2018;21(5):55–63.
Wang W, Xie E, Li X, Fan D-P, Song K, Liang D, et al. Pvt v2: improved baselines with pyramid vision transformer. Comput Vis Media. 2022;8(3):415–24.
Smith WA, Randall RB. Rolling element bearing diagnostics using the CaseWestern Reserve University data: a benchmark study. Mech Syst Signal Process. 2015;64:100–31.
Paderborn University bearing data center. Online. Accessed: Jan. 2021. 2014. https://mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing-datacenter/datasets-and-download.
Wang Y, Chao W-L, Weinberger KQ, Van Der Maaten L. Simpleshot: revisiting nearest-neighbor classification for few-shot learning. 2019. arXiv:1911.04623
Li W, Xu J, Huo J, Wang L, Gao Y, Luo J. Distribution consistency based covariance metric networks for few-shot learning. In: Proceedings of the AAAI conference on artificial intelligence. Vol. 33. 01. 2019. pp. 8642–8649.
Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM. Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. pp. 1199–1208.
Vu M-H, Nguyen V-Q, Tran T-T, Pham V-T, Lo M-T. Few-shot bearing fault diagnosis via ensembling transformer-based model with Mahalanobis distance metric learning from multiscale features. IEEE Trans Instrum Meas. 2024.
Than N-L, Nguyen VQ, Truong G-B, Pham V-T, Tran T-T. Mixmamba-fewshot: mamba and attention mixer-based method with few-shot learning for bearing fault diagnosis. Appl Intell. 2025;55(6):1–22.
Chen G, Zhou L, Zhang J, Yin X, Cui L, Dai Y. ESKNet: an enhanced adaptive selection kernel convolution for ultrasound breast tumors segmentation. Expert Syst Appl. 2024;246:123265.
Li Y, Hou Q, Zheng Z, Cheng M-M, Yang J, Li X. Large selective kernel network for remote sensing object detection. In: Proceedings of the IEEE/CVF international conference on computer vision. 2023. pp. 16794–16805.
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