{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:54:11Z","timestamp":1777704851520,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,6,21]]},"abstract":"<jats:p>Although the existing transfer learning method based on deep learning can realize bearing fault diagnosis under variable load working conditions, it is difficult to obtain bearing fault data and the training data of fault diagnosis model is insufficient\u00a3\u00acwhich leads to the low accuracy and generalization ability of fault diagnosis model, A fault diagnosis method based on improved elastic net transfer learning under variable load working conditions is proposed. The improved elastic net transfer learning is used to suppress the over fitting and improve the training efficiency of the model, and the long short-term memory network is introduced to train the fault diagnosis model, then a small amount of target domain data is used to fine tune the model parameters. Finally, the fault diagnosis model under variable load working conditions based on improved elastic net transfer learning is constructed. Finally, through model experiments and comparison with conventional deep learning fault diagnosis models such as long short-term memory network (LSTM), gated recurrent unit (GRU) and Bi-LSTM, it shows that the proposed method has higher accuracy and better generalization ability, which verifies the effectiveness of the method.<\/jats:p>","DOI":"10.3233\/jifs-210503","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T14:07:24Z","timestamp":1620742044000},"page":"12361-12369","source":"Crossref","is-referenced-by-count":11,"title":["A new bearing fault diagnosis method using elastic net transfer learning and LSTM"],"prefix":"10.1177","volume":"40","author":[{"given":"Xudong","family":"Song","sequence":"first","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dajie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pan","family":"Liang","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"An","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-210503_ref1","first-page":"2020","article-title":"Bearing Fault Diagnosis Method Based on EEMD and LSTM","volume":"15","author":"Zou","journal-title":"International Journal of Computers, Communications & Control"},{"key":"10.3233\/JIFS-210503_ref2","first-page":"1","article-title":"Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis Under Strong Noises and Variable Loads","volume":"99","author":"Qiao","year":"2020","journal-title":"IEEE Access"},{"issue":"1","key":"10.3233\/JIFS-210503_ref4","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks:An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Networks"},{"issue":"1","key":"10.3233\/JIFS-210503_ref5","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TIE.2014.2327555","article-title":"Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach","volume":"62","author":"Amar","year":"2015","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"2","key":"10.3233\/JIFS-210503_ref6","doi-asserted-by":"crossref","first-page":"425","DOI":"10.3390\/s17020425","article-title":"A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals","volume":"17","author":"Wei","year":"2017","journal-title":"Sensors"},{"key":"10.3233\/JIFS-210503_ref7","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.measurement.2015.08.034","article-title":"A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree","volume":"77","author":"Li","year":"2016","journal-title":"Measurement"},{"key":"10.3233\/JIFS-210503_ref8","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.measurement.2016.07.054","article-title":"Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis","volume":"93","author":"Guo","year":"2016","journal-title":"Measurement"},{"key":"10.3233\/JIFS-210503_ref10","doi-asserted-by":"crossref","first-page":"107227","DOI":"10.1016\/j.measurement.2019.107227","article-title":"An adaptive deep transfer learning method for bearing fault diagnosis","volume":"151","author":"Wu","year":"2019","journal-title":"Measurement"},{"key":"10.3233\/JIFS-210503_ref11","doi-asserted-by":"crossref","first-page":"164807","DOI":"10.1109\/ACCESS.2020.3022840","article-title":"A Generic Intelligent Bearing Fault Diagnosis System Using Convolutional Neural Networks With Transfer Learning","volume":"8","author":"Lu","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-210503_ref12","first-page":"1","article-title":"Transfer Learning Method Based on Adversarial Domain Adaption for Bearing Fault Diagnosis","volume":"99","author":"Shao","year":"2020","journal-title":"IEEE Access"},{"issue":"17","key":"10.3233\/JIFS-210503_ref15","first-page":"43","article-title":"A new image classification method using CNN transfer learning and web data augmentation","volume":"4174","author":"Dongmei","year":"2018","journal-title":"Expert Systems with Application"},{"key":"10.3233\/JIFS-210503_ref16","first-page":"1","article-title":"Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks","volume":"99","author":"Xu","year":"2019","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"5","key":"10.3233\/JIFS-210503_ref17","first-page":"196","article-title":"Flood Forecast Based on Regularized GRU Model","volume":"28","author":"Shengyue","year":"2019","journal-title":"Computer Systems & Applications"},{"issue":"15","key":"10.3233\/JIFS-210503_ref19","doi-asserted-by":"crossref","first-page":"8374","DOI":"10.1109\/JSEN.2019.2949057","article-title":"Knowledge transfer for rotary machine fault diagnosis","volume":"20","author":"Yan","year":"2020","journal-title":"IEEE Sensors Journal"},{"issue":"1","key":"10.3233\/JIFS-210503_ref20","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":"2019","journal-title":"IEEE Trans Syst, Man, Cybern, Syst"},{"key":"10.3233\/JIFS-210503_ref21","doi-asserted-by":"crossref","first-page":"14347","DOI":"10.1109\/ACCESS.2017.2720965","article-title":"Transfer learning with neural networks for bearing fault diagnosis in changing working conditions","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-210503_ref22","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":"10.3233\/JIFS-210503_ref23","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 Informat"},{"issue":"1","key":"10.3233\/JIFS-210503_ref25","first-page":"1","article-title":"Ball Mill Load Condition Recognition Model Based on Regularized Stochastic Configuration Networks","volume":"27","author":"Lijie","year":"2020","journal-title":"Control Engineering of China"},{"issue":"2","key":"10.3233\/JIFS-210503_ref26","first-page":"46","article-title":"The CNN-L1\/L2-ELM Hybrid Architecture Used to Classify Pulmonary Nodules","volume":"34","author":"Shufen","year":"2020","journal-title":"Journal of WUYI University"},{"issue":"06","key":"10.3233\/JIFS-210503_ref28","first-page":"456","article-title":"Bearing fault diagnosis based on unsupervised transfer component analysis and deep belief network","volume":"42","author":"Junjie","year":"2019","journal-title":"Journal of Wuhan University of Science and Technology"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-210503","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:10Z","timestamp":1777455730000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-210503"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,21]]},"references-count":21,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-210503","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,21]]}}}