{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T03:14:54Z","timestamp":1782962094494,"version":"3.54.5"},"reference-count":38,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Centre for Advances in Reliability and Safety (CAiRS), admitted under the AIR@InnoHK Research Cluster"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to solve the problem of imbalanced and noisy data samples for the fault diagnosis of rolling bearings, a novel ensemble capsule network (Capsnet) with a convolutional block attention module (CBAM) that is based on a weighted majority voting method is proposed in this study. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was used to decompose the raw vibration signal into different IMF signals, which are noise reduction signals. Secondly, the IMF signals were input into the Capsnet with CBAM in order to diagnose the fault category preliminarily. Finally, the weighted majority voting method was utilized so as to fuse all of the preliminary diagnosis results in order to obtain the final diagnostic decision. In order to verify the effectiveness of the proposed ensemble of Capsnet with CBAM, this method was applied to the fault diagnosis of rolling bearings with imbalanced and different SNR data samples. The diagnostic results show that the proposed diagnostic method can achieve higher levels of accuracy than other methods, such as single CNN, single Capsnet, ensemble CNN and an ensemble capsule network without CBAM and that it has stronger immunity to noise than an ensemble capsule network without CBAM.<\/jats:p>","DOI":"10.3390\/s22155543","type":"journal-article","created":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:17:27Z","timestamp":1658794647000},"page":"5543","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Ensemble Capsule Network with an Attention Mechanism for the Fault Diagnosis of Bearings from Imbalanced Data Samples"],"prefix":"10.3390","volume":"22","author":[{"given":"Zengbing","family":"Xu","sequence":"first","affiliation":[{"name":"Centre for Advances in Reliability and Safety, Hong Kong"},{"name":"School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8577-4547","authenticated-orcid":false,"given":"Carman","family":"Lee","sequence":"additional","affiliation":[{"name":"Centre for Advances in Reliability and Safety, Hong Kong"},{"name":"Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaqiong","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffery","family":"Chan","sequence":"additional","affiliation":[{"name":"Centre for Advances in Reliability and Safety, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7749","DOI":"10.1109\/TIE.2015.2460242","article-title":"Time-Varying and Multiresolution Envelope Analysis and Discriminative Feature Analysis for Bearing Fault Diagnosis","volume":"62","author":"Kang","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.jsv.2017.03.037","article-title":"Time-varying demodulation analysis for rolling bearing fault diagnosis under variable speed conditions","volume":"400","author":"Feng","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4548","DOI":"10.1109\/TIE.2009.2016517","article-title":"Diagnosis of Induction Machines\u2019 Rotor Faults in Time-Varying Conditions","volume":"56","author":"Stefani","year":"2009","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1007\/s00170-021-07253-6","article-title":"Harnessing fuzzy neural network for gear fault diagnosis with limited data labels","volume":"115","author":"Zhou","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ymssp.2015.10.025","article-title":"Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data","volume":"72\u201373","author":"Jia","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","first-page":"1","article-title":"A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions","volume":"70","author":"Han","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.ymssp.2018.07.048","article-title":"An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems","volume":"117","author":"Han","year":"2019","journal-title":"Mech. Syst. Sig. Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107233","DOI":"10.1016\/j.ymssp.2020.107233","article-title":"A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis","volume":"150","author":"Mao","year":"2021","journal-title":"Mech. Syst. Sig. Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"115002","DOI":"10.1088\/0957-0233\/26\/11\/115002","article-title":"Rolling bearing fault diagnosis using an optimization deep belief network","volume":"26","author":"Shao","year":"2015","journal-title":"Meas. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8680","DOI":"10.1109\/TIM.2020.2998233","article-title":"Deep focus parallel convolutional neural network for imbalanced classification of machinery fault diagnostics","volume":"69","author":"Duan","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jmsy.2018.04.005","article-title":"Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning","volume":"48","author":"Zhang","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9515","DOI":"10.1109\/ACCESS.2018.2890693","article-title":"Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: A comparative study","volume":"7","author":"Mao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108664","DOI":"10.1016\/j.ymssp.2021.108664","article-title":"Imbalanced fault diagnosis of rolling bearing using improved MsR-GAN and feature enhancement-driven CapsNet","volume":"168","author":"Liu","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108371","DOI":"10.1016\/j.measurement.2020.108371","article-title":"Rolling bearing fault diagnosis using variational autoencoding generative adversarial networks with deep regret analysis","volume":"168","author":"Liu","year":"2021","journal-title":"Measurement"},{"key":"ref_15","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural. Inform. Process. Syst."},{"key":"ref_16","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_17","unstructured":"Martin Arjovsky, S.C., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jprocont.2019.11.004","article-title":"Data supplement for a soft sensor using a new generative model based on a variational autoencoder and Wasserstein GAN","volume":"85","author":"Wang","year":"2020","journal-title":"J. Process Control."},{"key":"ref_19","first-page":"3856","article-title":"Hinton, G.E. Dynamic routing between capsules","volume":"30","author":"Sabour","year":"2017","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shahroudnejad, A., Mohammadi, A., and Plataniotis, K.N. (2018, January 26\u201329). Improved explainability of capsule networks: Relevance path by agreement. Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (Globalsip), Anaheim, CA, USA.","DOI":"10.1109\/GlobalSIP.2018.8646474"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14806","DOI":"10.1038\/s41598-021-94347-6","article-title":"Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis","volume":"11","author":"Osamor","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8394","DOI":"10.1109\/ACCESS.2018.2807121","article-title":"An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostic","volume":"6","author":"Wu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6943234","DOI":"10.1155\/2019\/6943234","article-title":"A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network","volume":"2019","author":"Wang","year":"2019","journal-title":"Complexity"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, L., Zhang, M., and Wang, K. (2020). A Fault Diagnostic Scheme Based on Capsule Network for Rolling Bearing under Different Rotational Speeds. Sensors, 20.","DOI":"10.3390\/s20071841"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109208","DOI":"10.1016\/j.measurement.2021.109208","article-title":"Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis","volume":"176","author":"Han","year":"2021","journal-title":"Measurement"},{"key":"ref_26","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":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neucom.2018.09.050","article-title":"A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis","volume":"323","author":"Zhu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2304","DOI":"10.1109\/TIM.2019.2958010","article-title":"Deep Ensemble Capsule Network for Intelligent Compound Fault Diagnosis Using Multisensory Data","volume":"69","author":"Huang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ning, D., and Feng, S. (2020). A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis. Appl. Sci., 10.","DOI":"10.3390\/app10103659"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4290","DOI":"10.1109\/TIE.2017.2762639","article-title":"Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes","volume":"65","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9602650","DOI":"10.1155\/2017\/9602650","article-title":"Fault feature extraction and diagnosis of gearbox based on EEMD and deep briefs network","volume":"2017","author":"Chen","year":"2017","journal-title":"Int. J. Rotating Mach."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101609","DOI":"10.1016\/j.aei.2022.101609","article-title":"Vibration signal-based early fault prognosis: Status quo and applications","volume":"52","author":"Lv","year":"2022","journal-title":"Adv. Eng. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3864","DOI":"10.1177\/09544062211043132","article-title":"Fault severity classification of ball bearing using SinGAN and deep convolutional neural network, Proceedings of the institution of mechanical engineers","volume":"236","author":"Akhenia","year":"2021","journal-title":"Part C J. Mech. Eng. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jin, Z., Chen, G., and Yang, Z. (2022). Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM. Entropy, 24.","DOI":"10.3390\/e24070927"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Silhavy, R., Silhavy, P., and Prokopova, Z. (2019). Intelligent Systems in Cybernetics and Automation Control Theory. CoMeSySo 2018. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-3-030-00184-1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1177\/0020294020981400","article-title":"Short-term wind speed prediction based on CEEMDAN-SE-improved PIO-GRNN model","volume":"54","author":"Ding","year":"2021","journal-title":"Meas. Control."},{"key":"ref_37","unstructured":"Park, J., Lee, J.-Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module, Sanghyun Woo. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.ymssp.2018.10.010","article-title":"The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data","volume":"120","author":"Daga","year":"2019","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5543\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:56:08Z","timestamp":1760140568000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,25]]},"references-count":38,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155543"],"URL":"https:\/\/doi.org\/10.3390\/s22155543","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,25]]}}}