{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T11:53:19Z","timestamp":1778586799575,"version":"3.51.4"},"reference-count":37,"publisher":"ASME International","issue":"4","license":[{"start":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T00:00:00Z","timestamp":1583971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51575102"],"award-info":[{"award-number":["51575102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012595","name":"Scientific Research Foundation of Graduate School of Southeast University","doi-asserted-by":"publisher","award":["YBPY1887"],"award-info":[{"award-number":["YBPY1887"]}],"id":[{"id":"10.13039\/501100012595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Unknown environmental noise and varying operation conditions negatively affect gear fault diagnosis (GFD) performance. In this paper, the sample\/feature hybrid transfer learning (TL) strategies are adopted for GFD under varying working conditions, where source working conditions are considered to help the learning of target working conditions. Here, a multiple domains-feature vector is extracted where certain insensitive features offset the adverse effects of varying working conditions on sensitive features, including time domain, frequency domain, noise domain, and torque domain. Before TL, the signed-rank and chi-square test-based similarity estimation frame is adopted to select source data sets, aiming to reduce the possibility of negative transfer. Then, the hybrid transfer model, including the fast TrAdaBoost and partial model-based transfer (PMT) algorithm, is carried out, whose weights are allocated in sample and feature, respectively. Related experiments were conducted on the drivetrain dynamics simulator, which proves that feature transfer is more suitable for low-quality source domains while sample transfer is more suitable for high-quality source domains. Compared with non-transfer strategy, transfer learning is a useful tool to solve a practical GFD problem when facing with multiple working conditions, thus enhancing the universality and application value in fault diagnosis field.<\/jats:p>","DOI":"10.1115\/1.4046337","type":"journal-article","created":{"date-parts":[[2020,2,14]],"date-time":"2020-02-14T14:13:00Z","timestamp":1581689580000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":11,"title":["Exploring Sample\/Feature Hybrid Transfer for Gear Fault Diagnosis Under Varying Working Conditions"],"prefix":"10.1115","volume":"20","author":[{"given":"Fei","family":"Shen","sequence":"first","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Xuanwu District, Nanjing 210096, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza","family":"Langari","sequence":"additional","affiliation":[{"name":"Engineering Technology and Industrial Distribution, Texas A&M University, College Station, TX 77843-3367"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruqiang","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Xuanwu District, Nanjing 210096, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2020,3,12]]},"reference":[{"issue":"5","key":"2020111705281835700_CIT0001","doi-asserted-by":"crossref","first-page":"4268","DOI":"10.1109\/TIE.2017.2767520","article-title":"Multiscale Filtering Reconstruction for Wind Turbine Gearbox Fault Diagnosis Under Varying-Speed and Noisy Conditions","volume":"65","author":"Wang","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"8","key":"2020111705281835700_CIT0002","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.measurement.2018.04.063","article-title":"Non-Stationary Vibration Feature Extraction Method Based on Sparse Decomposition and Order Tracking for Gearbox Fault Diagnosis","volume":"124","author":"Li","year":"2018","journal-title":"Measurement"},{"issue":"12","key":"2020111705281835700_CIT0003","doi-asserted-by":"crossref","first-page":"8338","DOI":"10.1016\/j.ijhydene.2017.02.151","article-title":"Instantaneous Power Factor Signature Analysis for Efficient Fault Diagnosis in Inverter Fed Three Phased Induction Motors","volume":"42","author":"Akar","year":"2017","journal-title":"Int. J. Hydrogen Energy"},{"issue":"3","key":"2020111705281835700_CIT0004","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1109\/TEC.2015.2423315","article-title":"Power Spectrum-Based Detection of Induction Motor Rotor Faults for Immunity to False Alarms","volume":"30","author":"Kim","year":"2015","journal-title":"IEEE Trans. Energy Convers."},{"issue":"4","key":"2020111705281835700_CIT0005","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.ymssp.2018.09.043","article-title":"HVSRMS Localization Formula and Localization Law: Localization Diagnosis of a Ball Bearing Outer Ring Fault","volume":"120","author":"Cui","year":"2019","journal-title":"Mech. Syst. Sig. Process."},{"issue":"5","key":"2020111705281835700_CIT0006","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ymssp.2015.11.014","article-title":"Construction of Hierarchical Diagnosis Network Based on Deep Learning and Its Application in the Fault Pattern Recognition of Rolling Element Bearings","volume":"72","author":"Gan","year":"2016","journal-title":"Mech. Syst. Sig. Process."},{"issue":"2","key":"2020111705281835700_CIT0007","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TCAD.2015.2459046","article-title":"Adaptive Board-Level Functional Fault Diagnosis Using Incremental Decision Trees","volume":"35","author":"Ye","year":"2016","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"issue":"7","key":"2020111705281835700_CIT0008","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.cageo.2017.03.007","article-title":"A Transfer Learning Method for Automatic Identification of Sandstone Microscopic Images","volume":"103","author":"Li","year":"2017","journal-title":"Comput. Geosci."},{"issue":"11","key":"2020111705281835700_CIT0009","doi-asserted-by":"crossref","first-page":"5214","DOI":"10.1109\/TIP.2018.2851067","article-title":"Semi-Supervised Deep Domain Adaptation via Coupled Neural Networks","volume":"27","author":"Ding","year":"2018","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"2020111705281835700_CIT0010","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TEVC.2017.2771451","article-title":"Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms","volume":"22","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"2020111705281835700_CIT0011","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/978-3-319-97982-3_16","article-title":"A Study on CNN Transfer Learning for Image Classification","volume":"840","author":"Hussain","year":"2019","journal-title":"Adv. Intell. Syst. Comput."},{"issue":"10","key":"2020111705281835700_CIT0012","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."},{"issue":"3","key":"2020111705281835700_CIT0013","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.tvjl.2017.12.026","article-title":"Use of Transfer Learning to Detect Diffuse Degenerative Hepatic Diseases From Ultrasound Images in Dogs: A Methodological Study","volume":"233","author":"Banzato","year":"2018","journal-title":"Vet. J."},{"issue":"1\u20132","key":"2020111705281835700_CIT0014","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s11263-013-0692-2","article-title":"Exploring Transfer Learning Approaches for Head Pose Classification From Multi-View Surveillance Images","volume":"109","author":"Rajagopal","year":"2014","journal-title":"Int. J. Comput. Vision"},{"issue":"3","key":"2020111705281835700_CIT0015","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1109\/TIE.2016.2627020","article-title":"Deep Model Based Domain Adaptation for Fault Diagnosis","volume":"64","author":"Lu","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"5","key":"2020111705281835700_CIT0016","doi-asserted-by":"crossref","first-page":"26241","DOI":"10.1109\/ACCESS.2018.2837621","article-title":"Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning","volume":"6","author":"Cao","year":"2018","journal-title":"IEEE Access"},{"issue":"4","key":"2020111705281835700_CIT0017","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. Informatics"},{"issue":"2","key":"2020111705281835700_CIT0018","first-page":"1","article-title":"Transfer Between Multiple Machine Plants: A Modified Fast Self-Organizing Feature Map and Two-Order Selective Ensemble Based Fault Diagnosis Strategy","volume":"151","author":"Shen","year":"2020","journal-title":"Measurement"},{"key":"2020111705281835700_CIT0019","doi-asserted-by":"crossref","DOI":"10.1109\/ICPHM.2016.7542845","article-title":"On Cross-Domain Feature Fusion in Gearbox Fault Diagnosis Under Various Operating Conditions Based on Transfer Component Analysis","author":"Xie","year":"2016"},{"key":"2020111705281835700_CIT0020","first-page":"81","article-title":"A Factor Analysis Based Transfer Learning Method for Gearbox Diagnosis Under Various Operating Conditions","author":"Wang","year":"2016"},{"issue":"1","key":"2020111705281835700_CIT0021","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1186\/s40537-016-0043-6","article-title":"A Survey of Transfer Learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"issue":"9","key":"2020111705281835700_CIT0022","first-page":"115368","article-title":"Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review","volume":"7","author":"Zheng","year":"2019","journal-title":"IEEE Access"},{"issue":"2018","key":"2020111705281835700_CIT0023","first-page":"1","article-title":"Machinery Bearing Fault Diagnosis Using Variational Mode Decomposition and Support Vector Machine as a Classifier","volume":"310","author":"Rama","year":"2018","journal-title":"IOP Conf. Ser.: Mater. Sci. Eng."},{"issue":"1","key":"2020111705281835700_CIT0024","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TIM.2017.2759418","article-title":"Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning","volume":"67","author":"Sun","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"2020111705281835700_CIT0025","doi-asserted-by":"crossref","first-page":"EL35","DOI":"10.1121\/1.4991329","article-title":"Incipient Fault Diagnosis in Bearings Under Variable Speed Conditions Using Multiresolution Analysis and a Weighted Committee Machine","volume":"142","author":"Tra","year":"2017","journal-title":"J. Acoust. Soc. Am."},{"issue":"2","key":"2020111705281835700_CIT0026","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s13198-016-0459-6","article-title":"Diagnosis of Bearing Defects in Induction Motors Using Discrete Wavelet Transform","volume":"9","author":"Bessous","year":"2018","journal-title":"Int. J. Syst. Assur. Eng. Manage."},{"issue":"10","key":"2020111705281835700_CIT0027","first-page":"1","article-title":"Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings","volume":"2017","author":"Verstraete","year":"2017","journal-title":"Shock and Vib."},{"issue":"5","key":"2020111705281835700_CIT0028","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1109\/TCYB.2018.2817630","article-title":"A Sequentially Truncated Higher Order Singular Value Decomposition-Based Algorithm for Tensor Completion","volume":"49","author":"Fang","year":"2019","journal-title":"IEEE Trans. Cybern."},{"issue":"4","key":"2020111705281835700_CIT0029","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1049\/iet-smt.2016.0176","article-title":"Slip Hankel Matrix Series-Based Singular Value Decomposition and Its Application for Fault Feature Extraction","volume":"11","author":"Xu","year":"2017","journal-title":"IET Sci., Meas. Technol."},{"issue":"5","key":"2020111705281835700_CIT0030","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1080\/02331888.2018.1469634","article-title":"Smoothed Alternatives of the Two-Sample Median and Wilcoxon\u2019s Rank Sum Tests","volume":"52","author":"Moriyama","year":"2018","journal-title":"Statistics"},{"issue":"4","key":"2020111705281835700_CIT0031","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1080\/00031305.2017.1360795","article-title":"On Mixture Alternatives and Wilcoxon\u2019s Signed-Rank Test","volume":"72","author":"Rosenblatt","year":"2018","journal-title":"Am. Stat."},{"issue":"4","key":"2020111705281835700_CIT0032","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1111\/trf.14057","article-title":"Understanding Tests of the Association of Categorical Variables: The Pearson Chi-Square Test and Fisher\u2019s Exact Test","volume":"57","author":"Hess","year":"2017","journal-title":"Transfusion"},{"issue":"3","key":"2020111705281835700_CIT0033","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1534\/genetics.117.300287","article-title":"A Powerful Variant-Set Association Test Based on Chi-Square Distribution","volume":"207","author":"Chen","year":"2017","journal-title":"Genetics"},{"issue":"1","key":"2020111705281835700_CIT0034","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"issue":"2","key":"2020111705281835700_CIT0035","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1142\/S242483551850025X","article-title":"The Observation of the Vein Distribution of a Partial Toe-Transfer Flaps with a Short Vascular Pedicle","volume":"23","author":"Kobayashi","year":"2018","journal-title":"J. Hand Surgery (Asian-Pacific Volume)"},{"issue":"2","key":"2020111705281835700_CIT0036","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.renene.2013.06.025","article-title":"Fault Diagnosis for a Wind Turbine Transmission System Based on Manifold Learning and Shannon Wavelet Support Vector Machine","volume":"62","author":"Tang","year":"2014","journal-title":"Renewable Energy"},{"issue":"4","key":"2020111705281835700_CIT0037","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ress.2018.02.012","article-title":"Reliable Multiple Combined Fault Diagnosis of Bearings Using Heterogeneous Feature Models and Multiclass Support Vector Machines","volume":"184","author":"Manjurul Islam","year":"2019","journal-title":"Reliab. Eng. Syst. Safe."}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/doi\/10.1115\/1.4046337\/6593931\/jcise_20_4_041009.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/doi\/10.1115\/1.4046337\/6593931\/jcise_20_4_041009.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T22:32:53Z","timestamp":1695767573000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/doi\/10.1115\/1.4046337\/1074558\/Exploring-SampleFeature-Hybrid-Transfer-for-Gear"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,12]]},"references-count":37,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,8,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4046337","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"value":"1530-9827","type":"print"},{"value":"1944-7078","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,12]]},"article-number":"041009"}}