{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:37:43Z","timestamp":1760524663183,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"General Project of the Zhejiang Provincial Department of Education","award":["Y202455248","QN25E050040"],"award-info":[{"award-number":["Y202455248","QN25E050040"]}]},{"name":"Zhejiang Provincial Youth Fund","award":["Y202455248","QN25E050040"],"award-info":[{"award-number":["Y202455248","QN25E050040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rolling bearings often produce non-stationary signals that are easily obscured by noise, particularly in high-noise environments, making fault detection a challenging task. To address this challenge, a novel fault diagnosis approach based on the Kolmogorov\u2013Arnold Network-based Hypergraph Message Passing (KAN-HyperMP) model is proposed. The KAN-HyperMP model is composed of three key components: a neighbor feature aggregation block, a feature fusion block, and a KANLinear block. Firstly, the neighbor feature aggregation block leverages hypergraph theory to integrate information from more distant neighbors, aiding in the reduction of noise impact, even when nearby neighbors are severely affected. Subsequently, the feature fusion block combines the features of these higher-order neighbors with the target node\u2019s own features, enabling the model to capture the complete structure of the hypergraph. Finally, the smoothness properties of B-spline functions within the Kolmogorov\u2013Arnold Network (KAN) are employed to extract critical diagnostic features from noisy signals. The proposed model is trained and evaluated on the Southeast University (SEU) and Jiangnan University (JNU) Datasets, achieving accuracy rates of 99.70% and 99.10%, respectively, demonstrating its effectiveness in fault diagnosis under both noise-free and noisy conditions.<\/jats:p>","DOI":"10.3390\/s24196448","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T07:30:18Z","timestamp":1728286218000},"page":"6448","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2884-4363","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Ocean Engineering, Yantai Institute of Science and Technology, Yantai 265600, China"}]},{"given":"Zhilin","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Engineering, Zhejiang Normal University, Jinhua 321004, China"}]},{"given":"Shuang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei 230601, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110739","DOI":"10.1016\/j.asoc.2023.110739","article-title":"A class-level matching unsupervised transfer learning network for rolling bearing fault diagnosis under various working conditions","volume":"146","author":"Huo","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"086001","DOI":"10.1088\/1361-6501\/ad41fb","article-title":"Rotating machinery fault classification based on one-dimensional residual network with attention mechanism and bidirectional gated recurrent unit","volume":"35","author":"Dong","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, M., Wang, W., Zhang, X., and Iu, H.H.C. (2022). A new fault diagnosis of rolling bearing based on Markov transition field and CNN. Entropy, 24.","DOI":"10.3390\/e24060751"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.ymssp.2017.06.022","article-title":"A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load","volume":"100","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102322","DOI":"10.1016\/j.aei.2023.102322","article-title":"Triplet attention-enhanced residual tree-inspired decision network: A hierarchical fault diagnosis model for unbalanced bearing datasets","volume":"59","author":"Cui","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6439","DOI":"10.1007\/s11071-024-09389-y","article-title":"An intelligent bearing fault diagnosis framework: One-dimensional improved self-attention-enhanced CNN and empirical wavelet transform","volume":"112","author":"Dong","year":"2024","journal-title":"Nonlinear Dyn."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108639","DOI":"10.1016\/j.knosys.2022.108639","article-title":"Deep multiple auto-encoder with attention mechanism network: A dynamic domain adaptation method for rotary machine fault diagnosis under different working conditions","volume":"249","author":"Yang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.neucom.2018.05.024","article-title":"An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition","volume":"310","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"130882","DOI":"10.1016\/j.energy.2024.130882","article-title":"Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems","volume":"294","author":"Yao","year":"2024","journal-title":"Energy"},{"key":"ref_10","first-page":"26256","article-title":"Learning physical dynamics with subequivariant graph neural networks","volume":"35","author":"Han","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"22496","DOI":"10.1021\/acsomega.3c00085","article-title":"SS-GNN: A simple-structured graph neural network for affinity prediction","volume":"8","author":"Zhang","year":"2023","journal-title":"ACS Omega"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"126441","DOI":"10.1016\/j.neucom.2023.126441","article-title":"A survey of graph neural network based recommendation in social networks","volume":"549","author":"Li","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wu, L., Chen, Y., Ji, H., and Liu, B. (2021, January 11\u201315). Deep learning on graphs for natural language processing. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Online.","DOI":"10.1145\/3404835.3462809"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhang, Y., and Wei, Q. (2022). Few-shot fine-grained image classification via GNN. Sensors, 22.","DOI":"10.3390\/s22197640"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108653","DOI":"10.1016\/j.ymssp.2021.108653","article-title":"The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study","volume":"168","author":"Li","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"12739","DOI":"10.1109\/TIE.2020.3040669","article-title":"Multireceptive field graph convolutional networks for machine fault diagnosis","volume":"68","author":"Li","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, C., Mo, L., and Yan, R. (2020, January 15\u201317). Rolling bearing fault diagnosis based on horizontal visibility graph and graph neural networks. Proceedings of the 2020 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD), Xi\u2019an, China.","DOI":"10.1109\/ICSMD50554.2020.9261687"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111697","DOI":"10.1016\/j.measurement.2022.111697","article-title":"Motor current signal analysis using hypergraph neural networks for fault diagnosis of electromechanical system","volume":"201","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110172","DOI":"10.1016\/j.knosys.2022.110172","article-title":"Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis","volume":"260","author":"Shi","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3229248","article-title":"Multiresolution hypergraph neural network for intelligent fault diagnosis","volume":"71","author":"Yan","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","unstructured":"Feng, Y., You, H., Zhang, Z., Ji, R., and Gao, Y. (February, January 27). Hypergraph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_22","first-page":"1509","article-title":"Hypergcn: A new method for training graph convolutional networks on hypergraphs","volume":"32","author":"Yadati","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_23","first-page":"1","article-title":"Hyperspectral image classification using feature fusion hypergraph convolution neural network","volume":"60","author":"Ma","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.patrec.2022.12.004","article-title":"SHCNet: A semi-supervised hypergraph convolutional networks based on relevant feature selection for hyperspectral image classification","volume":"165","author":"Sellami","year":"2023","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102663","DOI":"10.1016\/j.ipm.2021.102663","article-title":"Fair multi-stakeholder news recommender system with hypergraph ranking","volume":"58","author":"Gharahighehi","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.neucom.2022.09.102","article-title":"Motifs-based recommender system via hypergraph convolution and contrastive learning","volume":"512","author":"Sun","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"23680","DOI":"10.1109\/TITS.2022.3208943","article-title":"Dual dynamic spatial-temporal graph convolution network for traffic prediction","volume":"23","author":"Sun","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"123091","DOI":"10.1016\/j.eswa.2023.123091","article-title":"Learning spatial\u2013temporal pairwise and high-order relationships for short-term passenger flow prediction in urban rail transit","volume":"245","author":"Wu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, F., Pena-Pena, K., Qian, W., and Arce, G.R. (2024). T-HyperGNNs: Hypergraph neural networks via tensor representations. IEEE Trans. Neural Netw. Learn. Syst.","DOI":"10.36227\/techrxiv.21984797.v1"},{"key":"ref_30","unstructured":"Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Solja\u010di\u0107, M., Hou, T.Y., and Tegmark, M. (2024). Kan: Kolmogorov-arnold networks. arXiv."},{"key":"ref_31","first-page":"1","article-title":"Fault diagnosis of rolling bearing based on WHVG and GCN","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_32","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_33","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A new convolutional neural network-based data-driven fault diagnosis method","volume":"65","author":"Wen","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_35","unstructured":"Staudemeyer, R.C., and Morris, E.R. (2019). Understanding LSTM\u2014A tutorial into long short-term memory recurrent neural networks. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6448\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:11:18Z","timestamp":1760112678000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,5]]},"references-count":35,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196448"],"URL":"https:\/\/doi.org\/10.3390\/s24196448","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,10,5]]}}}