{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:51:47Z","timestamp":1740149507602,"version":"3.37.3"},"reference-count":42,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["51665013"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"crossref","award":["20212BAB204007"],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Jiangxi Province Graduate Student Innovation Project","award":["YC2021-S422"]},{"name":"Science Research Project of the Education Department of Jiangxi Province","award":["GJJ200616"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Given the complexity of the application scenarios of rolling bearing and the severe scarcity of fault samples, a solution to the issue of fault diagnosis under varying working conditions along with the absence of fault samples is required. A numerical model-driven cross-domain fault diagnosis method targeting variable working conditions is proposed based on the cross-Domain Nuisance Attribute Projection (cDNAP). Firstly, the simulation datasets consisting of multiple fault types under variable working conditions are constructed to solve the problem of incomplete fault samples. Secondly, the simulation datasets are expanded by means of generating adversarial network to ensure sufficient samples for subsequent model training. Finally, cDNAP is used to obtain the cross-domain simulation projection matrix, which eliminates the variance in the distribution of measured and simulated sample features under varying working conditions. The experimental results of cross-domain for variable working conditions show that the diagnostic accuracy reaches up to 99%. Compared with DANN, DSAN, and DAAN domain adversarial neural networks, the proposed method performs better in bearing fault diagnosis.<\/jats:p>","DOI":"10.3390\/s22249759","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T08:32:32Z","timestamp":1670920352000},"page":"9759","source":"Crossref","is-referenced-by-count":0,"title":["Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2976-1412","authenticated-orcid":false,"given":"Long","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Qian","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Lijuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Yanqing","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Haoyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Yu","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1016\/j.renene.2022.04.061","article-title":"A feature extraction and machine learning framework for bearing fault diagnosis","volume":"191","author":"Cui","year":"2022","journal-title":"Renew. Energ."},{"key":"ref_2","first-page":"1","article-title":"Integrating expert knowledge with domain adaptation for unsupervised fault diagnosis","volume":"71","author":"Wang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"87529","DOI":"10.1109\/ACCESS.2020.2992935","article-title":"Adaptive multiscale weighted permutation entropy for rolling bearing fault diagnosis","volume":"8","author":"Huo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"99756","DOI":"10.1109\/ACCESS.2021.3096723","article-title":"A novel rolling bearing fault diagnosis method based on adaptive feature selection and clustering","volume":"9","author":"Hou","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"168026","DOI":"10.1109\/ACCESS.2020.3023970","article-title":"Deep residual network for identifying bearing fault location and fault severity concurrently","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.jsv.2017.03.037","article-title":"Time-varying demodulation analysis for rolling bearing fault diagnosis under variable speed conditions","volume":"400","author":"Feng","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1109\/JSYST.2019.2929617","article-title":"An online bearing fault diagnosis technique via improved demodulation spectrum analysis under variable speed conditions","volume":"14","author":"Liu","year":"2019","journal-title":"IEEE Syst. J."},{"key":"ref_8","first-page":"1","article-title":"Instantaneous Frequency Synchronized Generalized Stepwise Demodulation Transform for Bearing Fault Diagnosis","volume":"71","author":"Shi","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","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_10","first-page":"1274380","article-title":"Fault diagnosis under variable working conditions based on STFT and transfer deep residual network","volume":"2020","author":"Du","year":"2020","journal-title":"Shock Vib."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"055107","DOI":"10.1088\/1361-6501\/ac4ffa","article-title":"A hidden feature label propagation method based on deep convolution variational autoencoder for fault diagnosis","volume":"33","author":"She","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4811","DOI":"10.1080\/00207543.2020.1808261","article-title":"A deformable CNN-DLSTM based transfer learning method for fault diagnosis of rolling bearing under multiple working conditions","volume":"59","author":"Wang","year":"2021","journal-title":"Int. J. Prod. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108765","DOI":"10.1016\/j.ymssp.2021.108765","article-title":"A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions","volume":"169","author":"Su","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102659","DOI":"10.1016\/j.simpat.2022.102659","article-title":"A fault diagnosis method of bearings based on deep transfer learning simulation modelling","volume":"122","author":"Huan","year":"2023","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110127","DOI":"10.1016\/j.measurement.2021.110127","article-title":"A novel tacholess order analysis method for bearings operating under time-varying speed conditions","volume":"186","author":"Choudhury","year":"2021","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.jsv.2017.08.003","article-title":"An adaptive and tacholess order analysis method based on enhanced empirical wavelet transform for fault detection of bearings with varying speeds","volume":"409","author":"Hu","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109749","DOI":"10.1016\/j.measurement.2021.109749","article-title":"A fault diagnosis method based on improved convolutional neural network for bearings under variable working conditions","volume":"182","author":"Zhang","year":"2021","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.neucom.2020.09.022","article-title":"An intelligent diagnosis framework for roller bearing fault under speed fluctuation condition","volume":"420","author":"Han","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_19","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_20","first-page":"2044","article-title":"Intelligent fault diagnosis method based on full 1-D convolutional generative adversarial network","volume":"16","author":"Guo","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"78056","DOI":"10.1109\/ACCESS.2022.3193244","article-title":"Rolling bearing fault diagnosis based on improved GAN and 2-D representation of acoustic emission signals","volume":"10","author":"Pham","year":"2022","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tong, Q., Lu, F., Feng, Z., Wan, Q., An, G., Cao, J., and Guo, T. (2022). A novel method for fault diagnosis of bearings with small and imbalanced data based on generative adversarial networks. Appl. Sci., 12.","DOI":"10.3390\/app12147346"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pei, X., Su, S., Jiang, L., Chu, C., Gong, L., and Yuan, Y. (2022). Research on Rolling Bearing Fault Diagnosis Method Based on Generative Adversarial and Transfer Learning. Processes, 10.","DOI":"10.3390\/pr10081443"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5714240","DOI":"10.1155\/2021\/5714240","article-title":"Bearing Fault Diagnosis under Variable Working Conditions Based on Deep Residual Shrinkage Networks and Transfer Learning","volume":"2021","author":"Yang","year":"2021","journal-title":"J. Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"177287","DOI":"10.1109\/ACCESS.2020.3025956","article-title":"A new transfer learning fault diagnosis method using TSC and JGSA under variable condition","volume":"8","author":"Yu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.isatra.2022.04.026","article-title":"Intelligent fault diagnosis of rolling bearings under varying operating conditions based on domain-adversarial neural network and attention mechanism","volume":"130","author":"Wu","year":"2022","journal-title":"ISA Trans."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"2182","DOI":"10.1177\/1475921720980718","article-title":"Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis","volume":"20","author":"Yu","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_30","first-page":"5760","article-title":"Simulation-driven domain adaptation for rolling element bearing fault diagnosis","volume":"18","author":"Liu","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ruan, D., Chen, Y., G\u00fchmann, C., Yan, J., and Li, Z. (2022). Dynamics Modeling of Bearing with Defect in Modelica and Application in Direct Transfer Learning from Simulation to Test Bench for Bearing Fault Diagnosis. Electronics, 11.","DOI":"10.3390\/electronics11040622"},{"key":"ref_32","unstructured":"Solomonoff, A., Campbell, W.M., and Quillen, C. (2007). Nuisance attribute projection. Speech Commun., 1\u201373."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"\u0160truc, V., Vesnicer, B., Miheli\u010d, F., and Pave\u0161i\u0107, N. (2010, January 14\u201319). Removing illumination artifacts from face images using the nuisance attribute projection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, USA.","DOI":"10.1109\/ICASSP.2010.5495203"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"786431","DOI":"10.1155\/2008\/786431","article-title":"The likelihood ratio decision criterion for nuisance attribute projection in GMM speaker verification","volume":"2008","author":"Vesnicer","year":"2008","journal-title":"EURASIP J. Adv. Sig. Pr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ymssp.2015.10.003","article-title":"Hidden Markov model and nuisance attribute projection based bearing performance degradation assessment","volume":"72","author":"Jiang","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"075101","DOI":"10.1088\/1361-6501\/ac57ef","article-title":"A simulation-data-driven subdomain adaptation adversarial transfer learning network for rolling element bearing fault diagnosis","volume":"33","author":"Zhu","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative adversarial networks: An overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE signal Proc. Mag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108371","DOI":"10.1016\/j.measurement.2020.108371","article-title":"Rolling bearing fault diagnosis using variational autoencoding generative adversarial networks with deep regret analysis","volume":"168","author":"Liu","year":"2021","journal-title":"Measurement"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, S., and Li, C. (2022). A Rolling Bearing Fault Diagnosis Based on Conditional Depth Convolution Countermeasure Generation Networks under Small Samples. Sensors, 22.","DOI":"10.3390\/s22155658"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107711","DOI":"10.1016\/j.ymssp.2021.107711","article-title":"Dynamic modeling and quantitative diagnosis for dual-impulse behavior of rolling element bearing with a spall on inner race","volume":"158","author":"Luo","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.jsv.2017.09.007","article-title":"Analytical validation of an explicit finite element model of a rolling element bearing with a localised line spall","volume":"416","author":"Singh","year":"2018","journal-title":"J. Sound Vib."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"148764","DOI":"10.1109\/ACCESS.2019.2944974","article-title":"A fault diagnosis method of industrial robot rolling bearing based on data driven and random intuitive fuzzy decision","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9759\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T07:49:41Z","timestamp":1736927381000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"references-count":42,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249759"],"URL":"https:\/\/doi.org\/10.3390\/s22249759","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,12,13]]}}}