{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T18:10:38Z","timestamp":1765303838866,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T00:00:00Z","timestamp":1648425600000},"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":["51805434","12172290"],"award-info":[{"award-number":["51805434","12172290"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Most cross-domain intelligent diagnosis approaches presume that the health states in training datasets are consistent with those in testing. However, it is usually difficult and expensive to collect samples under all failure states during the training stage in actual engineering; this causes the training dataset to be incomplete. These existing methods may not be favorably implemented with an incomplete training dataset. To address this problem, a novel deep-learning-based model called partial transfer ensemble learning framework (PT-ELF) is proposed in this paper. The major procedures of this study consist of three steps. First, the missing health states in the training dataset are supplemented by another dataset. Second, since the training dataset is drawn from two different distributions, a partial transfer mechanism is explored to train a weak global classifier and two partial domain adaptation classifiers. Third, a particular ensemble strategy combines these classifiers with different classification ranges and capabilities to obtain the final diagnosis result. Two case studies are used to validate our method. Results indicate that our method can provide robust diagnosis results based on an incomplete source domain under variable working conditions.<\/jats:p>","DOI":"10.3390\/s22072579","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:45:51Z","timestamp":1648590351000},"page":"2579","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Partial Transfer Ensemble Learning Framework: A Method for Intelligent Diagnosis of Rotating Machinery Based on an Incomplete Source Domain"],"prefix":"10.3390","volume":"22","author":[{"given":"Gang","family":"Mao","sequence":"first","affiliation":[{"name":"MIIT Key Laboratory of Dynamics and Control of Complex System, School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongzheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"MIIT Key Laboratory of Dynamics and Control of Complex System, School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8207-1045","authenticated-orcid":false,"given":"Sixiang","family":"Jia","sequence":"additional","affiliation":[{"name":"MIIT Key Laboratory of Dynamics and Control of Complex System, School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khandaker","family":"Noman","sequence":"additional","affiliation":[{"name":"MIIT Key Laboratory of Dynamics and Control of Complex System, School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2699-9951","authenticated-orcid":false,"given":"Yongbo","family":"Li","sequence":"additional","affiliation":[{"name":"MIIT Key Laboratory of Dynamics and Control of Complex System, School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.cja.2019.08.014","article-title":"Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging","volume":"33","author":"Yongbo","year":"2020","journal-title":"Chin. J. Aeronaut."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2017.10.024","article-title":"Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine","volume":"140","author":"Haidong","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_4","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_5","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 review and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.neucom.2015.04.069","article-title":"Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization","volume":"167","author":"Zhang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ma, K., and Ben-Arie, J. (2014, January 24\u201328). Compound exemplar based object detection by incremental random forest. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.417"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isatra.2018.04.005","article-title":"Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders","volume":"77","author":"Liu","year":"2018","journal-title":"ISA Trans."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.measurement.2016.04.007","article-title":"A sparse auto-encoder-based deep neural network approach for induction motor faults classification","volume":"89","author":"Sun","year":"2016","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Kim, Y.-H., and Choo, J. (2018, January 15\u201317). Intelligent fault detection via dilated convolutional neural networks. Proceedings of the 2018 IEEE International Conference on Big Data and Smart Computing, Shanghai, China.","DOI":"10.1109\/BigComp.2018.00137"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"3208","DOI":"10.1109\/TIE.2018.2844856","article-title":"Estimation of bearing remaining useful life based on multiscale convolutional neural network","volume":"66","author":"Zhu","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.knosys.2018.12.019","article-title":"A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults","volume":"165","author":"Han","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A Comprehensive Survey on Transfer Learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qian, W., Li, S., Wang, J., Xin, Y., and Ma, H. (2018, January 26\u201328). A New Deep Transfer Learning Network for Fault Diagnosis of Rotating Machine Under Variable Working Conditions. Proceedings of the 2018 Prognostics and System Health Management Conference (PHM-Chongqing), Chongqing, China.","DOI":"10.1109\/PHM-Chongqing.2018.00180"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/TII.2019.2917233","article-title":"Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network","volume":"16","author":"Chen","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"105107","DOI":"10.1088\/1361-6501\/ab230b","article-title":"A novel convolutional transfer feature discrimination network for imbalanced fault diagnosis under variable rotational speed","volume":"30","author":"Xu","year":"2019","journal-title":"Meas. Sci. Technol."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Li, X., Wen, L., Gao, L., and Gao, Y. (2019, January 22\u201326). Fault Diagnosis Using Unsupervised Transfer Learning Based on Adversarial Network. Proceedings of the 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, BC, Canada.","DOI":"10.1109\/COASE.2019.8842881"},{"key":"ref_23","unstructured":"Zhang, B., Li, W., Hao, J., Li, X.-L., and Zhang, M.J. (2018). Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, B., Shen, C., Yu, C., and Yang, Y. (2019, January 3\u20136). Data Fused Motor Fault Identification Based on Adversarial Auto-Encoder. Proceedings of the 2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Xi\u2019an, China.","DOI":"10.1109\/PEDG.2019.8807538"},{"key":"ref_25","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_27","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":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.knosys.2018.07.017","article-title":"A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes","volume":"160","author":"Jiao","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111168","DOI":"10.1109\/ACCESS.2019.2924003","article-title":"Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.ymssp.2018.03.025","article-title":"Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization","volume":"110","author":"Jia","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_31","unstructured":"Arjovsky, M., and Bottou, L.J. (2017). Towards principled methods for training generative adversarial networks. arXiv."},{"key":"ref_32","unstructured":"(2022, March 07). Available online: https:\/\/csegroups.case.edu\/bearingdatacenter\/home."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2579\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:44:43Z","timestamp":1760136283000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2579"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,28]]},"references-count":32,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072579"],"URL":"https:\/\/doi.org\/10.3390\/s22072579","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,3,28]]}}}