{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:54:30Z","timestamp":1781016870485,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"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":["61876059"],"award-info":[{"award-number":["61876059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate and fast rolling bearing fault diagnosis is required for the normal operation of rotating machinery and equipment. Although deep learning methods have achieved excellent results for rolling bearing fault diagnosis, the performance of most methods declines sharply when the working conditions change. To address this issue, we propose a one-dimensional lightweight deep subdomain adaptation network (1D-LDSAN) for faster and more accurate rolling bearing fault diagnosis. The framework uses a one-dimensional lightweight convolutional neural network backbone for the rapid extraction of advanced features from raw vibration signals. The local maximum mean discrepancy (LMMD) is employed to match the probability distribution between the source domain and the target domain data, and a fully connected neural network is used to identify the fault classes. Bearing data from the Case Western Reserve University (CWRU) datasets were used to validate the performance of the proposed framework under different working conditions. The experimental results show that the classification accuracy for 12 tasks was higher for the 1D-LDSAN than for mainstream transfer learning methods. Moreover, the proposed framework provides satisfactory results when a small proportion of the unlabeled target domain data is used for training.<\/jats:p>","DOI":"10.3390\/s22041624","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:34:47Z","timestamp":1645432487000},"page":"1624","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Transfer Learning Framework with a One-Dimensional Deep Subdomain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions"],"prefix":"10.3390","volume":"22","author":[{"given":"Ruixin","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Gu","sequence":"additional","affiliation":[{"name":"Guangdong Province Key Laboratory of Petrochemical Equipment Fault Diagnosis, Maoming 525000, China"},{"name":"Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China"},{"name":"Department of Chemistry, Institute of Inorganic and Analytical Chemistry, Goethe-University, Max-von-Laue-Str. 9, 60438 Frankfurt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"86510","DOI":"10.1109\/ACCESS.2020.2992692","article-title":"Convolutional Neural Network in Intelligent Fault Diagnosis toward Rotatory Machinery","volume":"8","author":"Tang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107174","DOI":"10.1016\/j.ymssp.2020.107174","article-title":"Rolling element bearing diagnosis based on singular value decomposition and composite squared envelope spectrum","volume":"148","author":"Lang","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.neucom.2018.06.078","article-title":"A survey on Deep Learning based bearing fault diagnosis","volume":"335","author":"Hoang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ymssp.2017.06.012","article-title":"A review on data-driven fault severity assessment in rolling bearings","volume":"99","author":"Cerrada","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","article-title":"Deep Learning Algorithms for Bearing Fault Diagnostics\u2014A Comprehensive Review","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106625","DOI":"10.1016\/j.ymssp.2020.106625","article-title":"Application of neural network algorithm in fault diagnosis of mechanical intelligence","volume":"141","author":"Xu","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s10732-014-9258-x","article-title":"Heuristic strategies for assessing wireless sensor network resiliency: An event-based formal approach","volume":"21","author":"Testa","year":"2015","journal-title":"J. Heuristics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109864","DOI":"10.1016\/j.measurement.2021.109864","article-title":"Extreme learning Machine-based classifier for fault diagnosis of rotating Machinery using a residual network and continuous wavelet transform","volume":"183","author":"Wei","year":"2021","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106679","DOI":"10.1016\/j.knosys.2020.106679","article-title":"Federated learning for machinery fault diagnosis with dynamic validation and self-supervision","volume":"213","author":"Zhang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"93155","DOI":"10.1109\/ACCESS.2020.2990528","article-title":"Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset with Deep Learning Approaches: A Review","volume":"8","author":"Neupane","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12361","DOI":"10.3233\/JIFS-210503","article-title":"A New Bearing Fault Diagnosis Method Using Elastic Net Transfer Learning and LSTM","volume":"40","author":"Song","year":"2021","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shen, C., Xie, J., Wang, D., Jiang, X., Shi, J., and Zhu, Z. (2019). Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault Diagnosis. Appl. Sci., 9.","DOI":"10.3390\/app9163374"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1109\/TIM.2019.2925247","article-title":"DCNN-Based Multi-Signal Induction Motor Fault Diagnosis","volume":"6","author":"Shao","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.ins.2020.06.060","article-title":"FaultFace: Deep Convolutional Generative Adversarial Network (DCGAN) based Ball-Bearing failure detection method","volume":"542","author":"Jv","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1109\/TIE.2016.2582729","article-title":"Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks","volume":"63","author":"Ince","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.neucom.2020.05.040","article-title":"Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data","volume":"409","author":"Cheng","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"66367","DOI":"10.1109\/ACCESS.2018.2878491","article-title":"Intelligent Fault Diagnosis Under Varying Working Conditions Based on Domain Adaptive Convolutional Neural Networks","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, K., Wei, Z., Xu, A., Zeng, P., and Yang, S. (2020). One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions. Sensors, 21.","DOI":"10.3390\/s20216039"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_20","unstructured":"Ganin, Y., and Lempitsky, V. (2014). Unsupervised domain adaptation by backpropagation. arXiv."},{"key":"ref_21","first-page":"8873960","article-title":"Bearing Fault Diagnosis Based on Multilayer Domain Adaptation","volume":"2020","author":"Yang","year":"2020","journal-title":"Shock Vib."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113710","DOI":"10.1016\/j.eswa.2020.113710","article-title":"A study on adaptation lightweight architecture based deep learning models for bearing fault diagnosis under varying working conditions","volume":"160","author":"Wu","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7445","DOI":"10.1109\/TII.2021.3054651","article-title":"Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning","volume":"17","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106962","DOI":"10.1016\/j.ymssp.2020.106962","article-title":"Residual joint adaptation adversarial network for intelligent transfer fault diagnosis","volume":"145","author":"Jiao","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TNNLS.2020.2988928","article-title":"Deep Subdomain Adaptation Network for Image Classification","volume":"32","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_26","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, 2.","DOI":"10.20944\/preprints201701.0132.v1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yu, D., and Gu, Y. (2021). A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose. Foods, 10.","DOI":"10.3390\/foods10040795"},{"key":"ref_28","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106396","DOI":"10.1016\/j.knosys.2020.106396","article-title":"Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions","volume":"207","author":"He","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_30","first-page":"315","article-title":"Deep Sparse Rectifier Neural Networks","volume":"15","author":"Glorot","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","first-page":"84","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Chen, Y., and Huang, M. (2019, January 8\u201311). Transfer Learning with Dynamic Adversarial Adaptation Network. Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), Beijing, China.","DOI":"10.1109\/ICDM.2019.00088"},{"key":"ref_35","unstructured":"Long, M., Cao, Y., Wang, J., and Jordan, M.I. (2015, January 6\u201311). Learning transferable features with deep adaptation networks. Proceedings of the 32nd International Conference on International Conference on Machine Learning (ICML\u201915), Lille, France."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7957","DOI":"10.1109\/TII.2021.3064377","article-title":"Universal Domain Adaptation in Fault Diagnostics with Hybrid Weighted Deep Adversarial Learning","volume":"17","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_37","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural net-works for mobile vision applications. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, CA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zheng, H., and Gu, Y. (2021). EnCNN-UPMWS: Waste Classification by a CNN Ensemble Using the UPM Weighting Strategy. Electronics, 10.","DOI":"10.3390\/electronics10040427"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain Adaptation via Transfer Component Analysis","volume":"22","author":"Pan","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"Tutorial on the Cross-Entropy Method","volume":"134","author":"Boer","year":"2005","journal-title":"Ann. Oper. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"107095","DOI":"10.1016\/j.ymssp.2020.107095","article-title":"Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions","volume":"147","author":"Li","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_43","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_44","unstructured":"(2022, January 21). Case Western Reserve University Bearing Dataset. Available online: https:\/\/engineering.case.edu\/bearingdatacenter."},{"key":"ref_45","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., and Darrell, T. (2014). Deep Domain Confusion: Maximizing for Domain Invariance. arXiv."},{"key":"ref_46","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"65303","DOI":"10.1109\/ACCESS.2019.2916935","article-title":"A Deep Transfer Model with Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis under Different Working Conditions","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_48","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Laurens","year":"2008","journal-title":"Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1624\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:22:46Z","timestamp":1760134966000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1624"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,18]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041624"],"URL":"https:\/\/doi.org\/10.3390\/s22041624","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,18]]}}}