Multiple Local-Global Correlation-Based Deep Neural Network with Selective Kernel Attention for Bearing Fault Diagnosis

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

Hoang D-T, Kang H-J. A survey on deep learning based bearing fault diagnosis. Neurocomputing. 2019;335:327–35.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Google Scholar 

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.

Article  Google Scholar 

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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