{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T03:16:07Z","timestamp":1781147767017,"version":"3.54.1"},"reference-count":24,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["52071245"],"award-info":[{"award-number":["52071245"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["61975157"],"award-info":[{"award-number":["61975157"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["2021YFB3202901"],"award-info":[{"award-number":["2021YFB3202901"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["52071245"],"award-info":[{"award-number":["52071245"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["61975157"],"award-info":[{"award-number":["61975157"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB3202901"],"award-info":[{"award-number":["2021YFB3202901"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain satisfactory accuracy. To overcome this drawback, this paper presents a long short-term memory (LSTM) neural network with transfer learning and ensemble learning and combines it with an unsupervised health indicator (HI) construction method for remaining-useful-life prediction. This study consists of the following parts: (1) utilizing the characteristics of deep belief networks and self-organizing map networks to translate raw sensor data to a synthetic HI that can effectively reflect system health; and (2) introducing transfer learning and ensemble learning to provide the required degradation mechanism for the RUL prediction model based on LSTM to improve the performance of the model. The performance of the proposed method is verified by two bearing datasets collected from experimental data, and the results show that the proposed method obtains better performance than comparable methods.<\/jats:p>","DOI":"10.3390\/s22155744","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T23:49:27Z","timestamp":1659397767000},"page":"5744","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Long Short-Term Memory Neural Network with Transfer Learning and Ensemble Learning for Remaining Useful Life Prediction"],"prefix":"10.3390","volume":"22","author":[{"given":"Lixiong","family":"Wang","sequence":"first","affiliation":[{"name":"National Engineering Research Center of Fiber Optic Sensing Technology and Networks, Wuhan University of Technology, Wuhan 430070, China"},{"name":"School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8453-1420","authenticated-orcid":false,"given":"Hanjie","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Fiber Optic Sensing Technology and Networks, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Pan","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Fiber Optic Sensing Technology and Networks, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2584-3608","authenticated-orcid":false,"given":"Dian","family":"Fan","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Fiber Optic Sensing Technology and Networks, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8863-7334","authenticated-orcid":false,"given":"Ciming","family":"Zhou","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Fiber Optic Sensing Technology and Networks, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhigang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.measurement.2013.11.012","article-title":"Condition monitoring and fault diagnosis of planetary gearboxes: A review","volume":"48","author":"Lei","year":"2014","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.neucom.2015.06.100","article-title":"Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors","volume":"188","author":"Ghods","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jiang, J., Lee, J., and Zeng, Y. 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