{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:32:09Z","timestamp":1762353129546,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T00:00:00Z","timestamp":1633651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61803227, 61773242, 61973184"],"award-info":[{"award-number":["61803227, 61773242, 61973184"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key\u00a0R\u00a0&amp;\u00a0D\u00a0Program\u00a0of\u00a0China","award":["2017YFB1302400"],"award-info":[{"award-number":["2017YFB1302400"]}]},{"name":"Major Agricultural Applied Technological Innovation Projects of Shandong Province","award":["SD2019NJ014"],"award-info":[{"award-number":["SD2019NJ014"]}]},{"DOI":"10.13039\/501100010084","name":"Independent Innovation Foundation of Shandong University","doi-asserted-by":"publisher","award":["2018ZQXM005"],"award-info":[{"award-number":["2018ZQXM005"]}],"id":[{"id":"10.13039\/501100010084","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Young Scholars Program of Shandong University, Weihai","award":["20820211010"],"award-info":[{"award-number":["20820211010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, intelligent fault diagnosis methods based on deep learning have developed rapidly. However, most of the existing work performs well under the assumption that training and testing samples are collected from the same distribution, and the performance drops sharply when the data distribution changes. For rolling bearings, the data distribution will change when the load and speed change. In this article, to improve fault diagnosis accuracy and anti-noise ability under different working loads, a transfer learning method based on multi-scale capsule attention network and joint distributed optimal transport (MSCAN-JDOT) is proposed for bearing fault diagnosis under different loads. Because multi-scale capsule attention networks can improve feature expression ability and anti-noise performance, the fault data can be better expressed. Using the domain adaptation ability of joint distribution optimal transport, the feature distribution of fault data under different loads is aligned, and domain-invariant features are learned. Through experiments that investigate bearings fault diagnosis under different loads, the effectiveness of MSCAN-JDOT is verified; the fault diagnosis accuracy is higher than that of other methods. In addition, fault diagnosis experiment is carried out in different noise environments to demonstrate MSCAN-JDOT, which achieves a better anti-noise ability than other transfer learning methods.<\/jats:p>","DOI":"10.3390\/s21196696","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:37:49Z","timestamp":1633901869000},"page":"6696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi-Scale Capsule Attention Network and Joint Distributed Optimal Transport for Bearing Fault Diagnosis under Different Working Loads"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6337-1712","authenticated-orcid":false,"given":"Zihao","family":"Sun","sequence":"first","affiliation":[{"name":"School of Mechanical Electrical and Information Engineering, Shandong University, Weihai 264209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianfeng","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Mechanical Electrical and Information Engineering, Shandong University, Weihai 264209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1560-4563","authenticated-orcid":false,"given":"Xu","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Mechanical Electrical and Information Engineering, Shandong University, Weihai 264209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0066-2114","authenticated-orcid":false,"given":"Chengjin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Electrical and Information Engineering, Shandong University, Weihai 264209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106272","DOI":"10.1016\/j.ymssp.2019.106272","article-title":"Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine","volume":"133","author":"Chen","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1109\/TIM.2019.2925247","article-title":"DCNN-Based Multi-Signal Induction Motor Fault Diagnosis","volume":"69","author":"Shao","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TIM.2019.2902003","article-title":"Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks","volume":"69","author":"Xu","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/TII.2020.2991796","article-title":"Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction","volume":"17","author":"Ma","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5581","DOI":"10.1109\/JSEN.2017.2726011","article-title":"Fault Diagnosis of a Rolling Bearing Using Wavelet Packet De-noising and Random Forests","volume":"17","author":"Wang","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6248","DOI":"10.1109\/TIE.2020.2994868","article-title":"Fault Diagnosis of an Autonomous Vehicle with an Improved SVM Algorithm Subject to Unbalanced Datasets","volume":"68","author":"Shi","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106587","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of machine learning to machine fault diagnosis: A re-view and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_8","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_9","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. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, W., Peng, G., Li, C., Chen, Y., and Zhang, Z. (2017). A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals. Sensors, 17.","DOI":"10.20944\/preprints201701.0132.v1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"055402","DOI":"10.1088\/1361-6501\/ab0793","article-title":"A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network","volume":"30","author":"Ma","year":"2019","journal-title":"Meas. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2446","DOI":"10.1109\/TII.2018.2864759","article-title":"Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning","volume":"15","author":"Shao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8374","DOI":"10.1109\/JSEN.2019.2949057","article-title":"Knowledge Transfer for Rotary Machine Fault Diagnosis","volume":"20","author":"Yan","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neucom.2019.04.010","article-title":"Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method","volume":"352","author":"An","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6798","DOI":"10.1109\/TII.2020.3045002","article-title":"A Stacked Auto-Encoder Based Partial Adversarial Domain Adaptation Model for Intelligent Fault Diagnosis of Rotating Machines","volume":"17","author":"Liu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2833","DOI":"10.1109\/TII.2020.3008010","article-title":"Intelligent Fault Diagnosis by Fusing Domain Adversarial Training and Maximum Mean Discrepancy via Ensemble Learning","volume":"17","author":"Li","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/TSMC.2017.2754287","article-title":"A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis","volume":"49","author":"Wen","year":"2017","journal-title":"IEEE Trans. Syst. Man, Cybern. Syst."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"8702","DOI":"10.1109\/TIM.2020.2995441","article-title":"Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery","volume":"69","author":"Chen","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3041105","article-title":"An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings Under Polytropic Working Conditions","volume":"70","author":"Lei","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3123218","article-title":"Intelligent Fault Diagnosis with Deep Adversarial Domain Adaptation","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","first-page":"1","article-title":"Conditional Adversarial Domain Adaptation with Discrimination Embedding for Locomotive Fault Diagnosis","volume":"70","author":"Yu","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_23","first-page":"1","article-title":"Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","first-page":"1","article-title":"Deep Adversarial Capsule Network for Compound Fault Diagnosis of Machinery Toward Multidomain Generalization Task","volume":"70","author":"Huang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3118090","article-title":"Optimal Transport Based Deep Domain Adaptation Approach for Fault Diagnosis of Rotating Machine","volume":"70","author":"Liu","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_26","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"2","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process."},{"key":"ref_27","unstructured":"Sabour, S., Frosst, N., and Hinton, G. (2017, January 4\u20139). Dynamic routing between capsules. Proceedings of the 31st Conference on Neural Information Processing System, Long Beach, CA, USA."},{"key":"ref_28","unstructured":"Hinton, G., Sabour, S., and Frosst, N. (May, January 30). Matrix capsules with EM routing. Proceedings of the 6th International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_29","unstructured":"Ribeiro, F.D.S., Leontidis, G., and Kollias, S. (2020, January 7\u201312). Capsule Routing via Variational Bayes. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1109\/TPAMI.2016.2615921","article-title":"Optimal Transport for Domain Adaptation","volume":"39","author":"Courty","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Courty, N., Flamary, R., Habrard, A., and Rakotomamonjy, A. (2017, January 4\u20139). Joint distribution optimal transportation for domain adaptation. Proceedings of the Conference on Advances in Neural Information Processing Systems, Long Beach, CA, USA.","DOI":"10.1109\/TPAMI.2016.2615921"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/978-3-030-01225-0_28","article-title":"DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation","volume":"Volume 11208","author":"Damodaran","year":"2018","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2018"},{"key":"ref_33","unstructured":"(2021, March 29). Case Western Reserve University Bearing Data Center Website. Available online: https:\/\/csegroups.case.edu\/bearingdatacenter\/pages\/welcome-case-western-reserve-university-bearing-data-center-website."},{"key":"ref_34","first-page":"1","article-title":"Domain-Adversarial Training of Neural Networks","volume":"17","author":"Ganin","year":"2017","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6696\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:10:43Z","timestamp":1760166643000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,8]]},"references-count":34,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21196696"],"URL":"https:\/\/doi.org\/10.3390\/s21196696","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,10,8]]}}}