{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:36:48Z","timestamp":1765233408328,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R and D Program of China","doi-asserted-by":"publisher","award":["2020YFB1713300","[2015]4011","[2017]5788","[2020]005","[2020]009","QJH KY [2022]142","2021)74"],"award-info":[{"award-number":["2020YFB1713300","[2015]4011","[2017]5788","[2020]005","[2020]009","QJH KY [2022]142","2021)74"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guizhou Science and Technology Planning Project","award":["2020YFB1713300","[2015]4011","[2017]5788","[2020]005","[2020]009","QJH KY [2022]142","2021)74"],"award-info":[{"award-number":["2020YFB1713300","[2015]4011","[2017]5788","[2020]005","[2020]009","QJH KY [2022]142","2021)74"]}]},{"name":"Talent Training Base Project of Colleges and Universities in Guizhou Province","award":["2020YFB1713300","[2015]4011","[2017]5788","[2020]005","[2020]009","QJH KY [2022]142","2021)74"],"award-info":[{"award-number":["2020YFB1713300","[2015]4011","[2017]5788","[2020]005","[2020]009","QJH KY [2022]142","2021)74"]}]},{"name":"Guizhou Provincial Department of Education Youth Science and Technology Talent Growth Project","award":["2020YFB1713300","[2015]4011","[2017]5788","[2020]005","[2020]009","QJH KY [2022]142","2021)74"],"award-info":[{"award-number":["2020YFB1713300","[2015]4011","[2017]5788","[2020]005","[2020]009","QJH KY [2022]142","2021)74"]}]},{"name":"Scientific Research Project of Introducing Talents of Guizhou University","award":["2020YFB1713300","[2015]4011","[2017]5788","[2020]005","[2020]009","QJH KY [2022]142","2021)74"],"award-info":[{"award-number":["2020YFB1713300","[2015]4011","[2017]5788","[2020]005","[2020]009","QJH KY [2022]142","2021)74"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>At present, the success of most intelligent fault diagnosis methods is heavily dependent on large datasets of artificial simulation faults (ASF), which have not been widely used in practice because it is often costly to obtain a large number of samples in reality. Fortunately, various faults can be easily simulated in the laboratory, and these simulated faults contain a lot of fault diagnosis knowledge. In this study, based on a Siamese network framework, we propose a bearing fault diagnosis based on few-shot transfer learning across different datasets (cross-machine), using the knowledge of ASF to diagnose bearings with natural faults (NF). First of all, the model obtains a good feature encoder in the source domain, then defines a fault support set for comparison, and finally adjusts the support set with a very small number of target domain samples to improve the fault diagnosis performance of the model. We carried out experimental verification from many aspects on the ASF and NF datasets provided by Case Western Reserve University (CWRU) and Paderborn University (PU). The results show that the proposed method can fully learn diagnostic knowledge in different ASF datasets and sample numbers, and effectively use this knowledge to accurately identify the health state of the NF bearing, which has strong generalization and robustness. Our method does not need second training, which may be more convenient in some practical applications. Finally, we also discuss the possible limitations of this method.<\/jats:p>","DOI":"10.3390\/e24091295","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T20:50:45Z","timestamp":1663188645000},"page":"1295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Novel Bearing Fault Diagnosis Method Based on Few-Shot Transfer Learning across Different Datasets"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6070-7874","authenticated-orcid":false,"given":"Yizong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4759-6000","authenticated-orcid":false,"given":"Shaobo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5888-1184","authenticated-orcid":false,"given":"Ansi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanjiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Shi, H., Fu, W., Li, B., Shao, K., and Yang, D. (2021). Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization. Entropy, 23.","DOI":"10.3390\/e23050527"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, D., Wang, Q., Tao, J., Li, G., and Wu, J. (2018, January 25\u201327). Fault Diagnosis Method Based on Improved Deep Boltzmann Machines. Proceedings of the 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), Enshi, China.","DOI":"10.1109\/DDCLS.2018.8516106"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105971","DOI":"10.1016\/j.knosys.2020.105971","article-title":"Intelligent Fault Diagnosis of Rolling Bearings Based on Normalized CNN Considering Data Imbalance and Variable Working Conditions","volume":"199","author":"Zhao","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104837","DOI":"10.1016\/j.knosys.2019.07.008","article-title":"Deep Learning Fault Diagnosis Method Based on Global Optimization GAN for Unbalanced Data","volume":"187","author":"Zhou","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, S., Wang, D., Kong, D., Wang, J., Li, W., and Zhou, S. (2020). Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning. Sensors, 20.","DOI":"10.3390\/s20226437"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/TR.2019.2942049","article-title":"A Local Adaptive Minority Selection and Oversampling Method for Class-Imbalanced Fault Diagnostics in Industrial Systems","volume":"69","author":"Wu","year":"2020","journal-title":"IEEE Trans. Reliab."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"70111","DOI":"10.1109\/ACCESS.2020.2986356","article-title":"Data Fusion Generative Adversarial Network for Multi-Class Imbalanced Fault Diagnosis of Rotating Machinery","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ins.2020.07.014","article-title":"RCSMOTE: Range-Controlled Synthetic Minority over-Sampling Technique for Handling the Class Imbalance Problem","volume":"542","author":"Soltanzadeh","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.jmsy.2020.10.007","article-title":"Intelligent Fault Diagnosis of Mechanical Equipment under Varying Working Condition via Iterative Matching Network Augmented with Selective Signal Reuse Strategy","volume":"57","author":"Zhang","year":"2020","journal-title":"J. Manuf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2497","DOI":"10.1007\/s11071-021-06393-4","article-title":"ANS-Net: Anti-Noise Siamese Network for Bearing Fault Diagnosis with a Few Data","volume":"104","author":"Fang","year":"2021","journal-title":"Nonlinear Dyn."},{"key":"ref_13","unstructured":"Lu, N., Hu, H., Yin, T., Lei, Y., and Wang, S. (2021). Transfer Relation Network for Fault Diagnosis of Rotating Machinery with Small Data. IEEE Trans. Cybern., 1\u201315."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102781","DOI":"10.1016\/j.cviu.2019.07.001","article-title":"Attentive Matching Network for Few-Shot Learning","volume":"187","author":"Mai","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5131","DOI":"10.1177\/0954406219840381","article-title":"Transfer Learning with Convolutional Neural Networks for Small Sample Size Problem in Machinery Fault Diagnosis","volume":"233","author":"Xiao","year":"2019","journal-title":"Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isatra.2021.02.042","article-title":"Intelligent Fault Diagnosis of Machines with Small & Imbalanced Data: A State-of-the-Art Review and Possible Extensions","volume":"119","author":"Zhang","year":"2021","journal-title":"ISA Trans."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106608","DOI":"10.1016\/j.ymssp.2019.106608","article-title":"A Novel Model with the Ability of Few-Shot Learning and Quick Updating for Intelligent Fault Diagnosis","volume":"138","author":"Ren","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110895","DOI":"10.1109\/ACCESS.2019.2934233","article-title":"Limited Data Rolling Bearing Fault Diagnosis with Few-Shot Learning","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"91216","DOI":"10.1109\/ACCESS.2019.2926234","article-title":"A Deep Transfer Nonnegativity-Constraint Sparse Autoencoder for Rolling Bearing Fault Diagnosis with Few Labeled Data","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.isatra.2021.03.013","article-title":"Semi-Supervised Meta-Learning Networks with Squeeze-and-Excitation Attention for Few-Shot Fault Diagnosis","volume":"120","author":"Feng","year":"2022","journal-title":"ISA Trans."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.neucom.2021.01.099","article-title":"Meta-Learning for Few-Shot Bearing Fault Diagnosis under Complex Working Conditions","volume":"439","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5393","DOI":"10.1007\/s00521-020-05345-0","article-title":"Multi-Label Fault Diagnosis of Rolling Bearing Based on Meta-Learning","volume":"33","author":"Yu","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107510","DOI":"10.1016\/j.ymssp.2020.107510","article-title":"Metric-Based Meta-Learning Model for Few-Shot Fault Diagnosis under Multiple Limited Data Conditions","volume":"155","author":"Wang","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"84007","DOI":"10.1088\/1361-6501\/abe5e3","article-title":"Data Augmentation for Rolling Bearing Fault Diagnosis Using an Enhanced Few-Shot Wasserstein Auto-Encoder with Meta-Learning","volume":"32","author":"Pei","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4754","DOI":"10.1109\/TIA.2021.3091958","article-title":"Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning","volume":"57","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106829","DOI":"10.1016\/j.knosys.2021.106829","article-title":"Similarity-Based Meta-Learning Network with Adversarial Domain Adaptation for Cross-Domain Fault Identification","volume":"217","author":"Feng","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.neucom.2020.11.070","article-title":"An Intelligent Fault Diagnosis Model Based on Deep Neural Network for Few-Shot Fault Diagnosis","volume":"456","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105313","DOI":"10.1016\/j.knosys.2019.105313","article-title":"Deep Transfer Multi-Wavelet Auto-Encoder for Intelligent Fault Diagnosis of Gearbox with Few Target Training Samples","volume":"191","author":"He","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109553","DOI":"10.1016\/j.measurement.2021.109553","article-title":"Transfer Learning Method for Bearing Fault Diagnosis Based on Fully Convolutional Conditional Wasserstein Adversarial Networks","volume":"180","author":"Liu","year":"2021","journal-title":"Measurement"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108202","DOI":"10.1016\/j.measurement.2020.108202","article-title":"Few-Shot Transfer Learning for Intelligent Fault Diagnosis of Machine","volume":"166","author":"Wu","year":"2020","journal-title":"Measurement"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.ymssp.2018.12.051","article-title":"An Intelligent Fault Diagnosis Approach Based on Transfer Learning from Laboratory Bearings to Locomotive Bearings","volume":"122","author":"Yang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7316","DOI":"10.1109\/TIE.2018.2877090","article-title":"Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines with Unlabeled Data","volume":"66","author":"Guo","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_33","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_34","unstructured":"(2021, July 08). Case Western Reserve University Bearing Data Center Website. Available online: https:\/\/engineering.case.edu\/bearingdatacenter\/welcome."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lessmeier, C., Kimotho, J.K., Zimmer, D., and Sextro, W. (2016, January 19\u201321). Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification. Proceedings of the PHM Society European Conference 2016, Chengdu, China.","DOI":"10.36001\/phme.2016.v3i1.1577"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"80937","DOI":"10.1109\/ACCESS.2019.2921480","article-title":"Domain Adaptive Motor Fault Diagnosis Using Deep Transfer Learning","volume":"7","author":"Xiao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"085104","DOI":"10.1088\/1361-6501\/ab6ade","article-title":"Cross-Domain Learning in Rotating Machinery Fault Diagnosis under Various Operating Conditions Based on Parameter Transfer","volume":"31","author":"Li","year":"2020","journal-title":"Meas. Sci. Technol."},{"key":"ref_38","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_39","first-page":"1511","article-title":"A Siamese Hybrid Neural Network Framework for Few-Shot Fault Diagnosis of Fixed-Wing Unmanned Aerial Vehicles","volume":"9","author":"Li","year":"2022","journal-title":"J. Comput. Des. Eng."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/9\/1295\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:31:22Z","timestamp":1760142682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/9\/1295"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"references-count":39,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["e24091295"],"URL":"https:\/\/doi.org\/10.3390\/e24091295","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2022,9,14]]}}}