{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T20:07:47Z","timestamp":1770581267141,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T00:00:00Z","timestamp":1697068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Foundation of State Key Laboratory","award":["6142004200501"],"award-info":[{"award-number":["6142004200501"]}]},{"name":"Science and Technology Foundation of State Key Laboratory","award":["MJ-2018-Y-58"],"award-info":[{"award-number":["MJ-2018-Y-58"]}]},{"name":"Science and Technology Foundation of State Key Laboratory","award":["YWF-22-L-516"],"award-info":[{"award-number":["YWF-22-L-516"]}]},{"name":"Science and Technology Foundation of State Key Laboratory","award":["51575021"],"award-info":[{"award-number":["51575021"]}]},{"name":"Civil Aircraft Special Research Project","award":["6142004200501"],"award-info":[{"award-number":["6142004200501"]}]},{"name":"Civil Aircraft Special Research Project","award":["MJ-2018-Y-58"],"award-info":[{"award-number":["MJ-2018-Y-58"]}]},{"name":"Civil Aircraft Special Research Project","award":["YWF-22-L-516"],"award-info":[{"award-number":["YWF-22-L-516"]}]},{"name":"Civil Aircraft Special Research Project","award":["51575021"],"award-info":[{"award-number":["51575021"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["6142004200501"],"award-info":[{"award-number":["6142004200501"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["MJ-2018-Y-58"],"award-info":[{"award-number":["MJ-2018-Y-58"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["YWF-22-L-516"],"award-info":[{"award-number":["YWF-22-L-516"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["51575021"],"award-info":[{"award-number":["51575021"]}]},{"name":"National Natural Science Foundation of China","award":["6142004200501"],"award-info":[{"award-number":["6142004200501"]}]},{"name":"National Natural Science Foundation of China","award":["MJ-2018-Y-58"],"award-info":[{"award-number":["MJ-2018-Y-58"]}]},{"name":"National Natural Science Foundation of China","award":["YWF-22-L-516"],"award-info":[{"award-number":["YWF-22-L-516"]}]},{"name":"National Natural Science Foundation of China","award":["51575021"],"award-info":[{"award-number":["51575021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>The precise remaining useful life (RUL) prediction of turbofan engines benefits maintenance decisions. The training data quantity and quality are crucial for effective prediction modeling and accuracy improvement. However, the performance degradation process of the same type of turbofan engine usually exhibits different trajectories because of engines\u2019 differences in degradation degrees, degradation rates, and initial health states. In addition, the initial part of the trajectory is a stationary health stage, which contains very little information on degradation and is not helpful for modeling. Considering the differential degradation characteristics and the requirement for accurate prediction modeling of the same type of turbofan engines with individual differences, we specifically propose a personalized transfer learning framework for RUL prediction by answering three key questions: when, what, and how to transfer in prediction modeling. The framework tries to maximumly utilize multi-source-domain data (samples of the same type of engines that run to failure) to improve the training data quantity and quality. Firstly, a transfer time identification method based on a dual-baseline performance assessment and the Wasserstein distance is designed to eliminate the worthless part of a trajectory for transfer and prediction modeling. Then, the transferability of each sample in the multi-source domain is measured by an approach, named the time-lag ensemble distance measurement, and then the source domain is ranked and adaptively deconstructed into two parts according to transferability. Ultimately, a new training loss function considering the transferability of the weighted multi-source-domain data and a two-stage transfer learning scheme is introduced into an informer-based RUL prediction model, which has a great advantage for long-time-series prediction. The simulation data of 100 of the same type of turbofan engine with individual differences and five comparison experiments validate the effectiveness and accuracy of the proposed method.<\/jats:p>","DOI":"10.3390\/axioms12100963","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T14:22:17Z","timestamp":1697120537000},"page":"963","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Personalized Transfer Learning Framework for Remaining Useful Life Prediction Using Adaptive Deconstruction and Dynamic Weight Informer"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0073-0225","authenticated-orcid":false,"given":"Xue","family":"Liu","sequence":"first","affiliation":[{"name":"School of Reliability and System Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Reliability and System Engineering, Beihang University, Beijing 100191, China"},{"name":"Institute of Reliability Engineering, Beihang University, Beijing 100191, China"},{"name":"Science & Technology Laboratory on Reliability & Environmental Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3460-385X","authenticated-orcid":false,"given":"Dengwei","family":"Song","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kayid, M., Alshagrawi, L., and Shrahili, M. (2023). Stochastic Ordering Results on Implied Lifetime Distributions under a Specific Degradation Model. Axioms, 12.","DOI":"10.20944\/preprints202307.0208.v1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1007\/s00170-022-09280-3","article-title":"Similarity-based prediction method for machinery remaining useful life: A review","volume":"121","author":"Xue","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s13198-013-0195-0","article-title":"Remaining useful life estimation: Review","volume":"5","author":"Ahmadzadeh","year":"2014","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/TASE.2009.2038170","article-title":"Health-state estimation and prognostics in machining processes","volume":"7","author":"Camci","year":"2010","journal-title":"IEEE. Trans. Autom. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Askari, B., Bozza, A., Cavone, G., Carli, R., and Dotoli, M. (2023). An Adaptive Constrained Clustering Approach for Real-Time Fault Detection of Industrial Systems. Eur. J. Control.","DOI":"10.1016\/j.ejcon.2023.100858"},{"key":"ref_6","first-page":"4835","article-title":"A Machine Learning Approach to Fault Prediction of Power Distribution Grids under Heatwaves","volume":"59","author":"Atrigna","year":"2023","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.promfg.2020.06.015","article-title":"Remaining useful life prediction using deep learning approaches: A review","volume":"49","author":"Wang","year":"2020","journal-title":"Procedia Manuf."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Zhao, Y. (2022). Multi-Scale Remaining Useful Life Prediction Using Long Short-Term Memory. Sustainability, 14.","DOI":"10.3390\/su142315667"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhao, Y., and Addepalli, S. (2021). Practical options for adopting recurrent neural network and its variants on remaining useful life prediction. Chin. J. Mech. Eng., 34.","DOI":"10.1186\/s10033-021-00588-x"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mou, Q., Wei, L., Wang, C., Luo, D., He, S., Zhang, J., Xu, H., Luo, C., and Gao, C. (2021). Unsupervised domain-adaptive scene-specific pedestrian detection for static video surveillance. Pattern Recognit., 118.","DOI":"10.1016\/j.patcog.2021.108038"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alhudhaif, A., Polat, K., and Karaman, O. (2021). Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images. Expert. Syst. Appl., 180.","DOI":"10.1016\/j.eswa.2021.115141"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Deng, Z., Wang, Z., Tang, Z., Huang, K., and Zhu, H. (2021). A deep transfer learning method based on stacked autoencoder for cross-domain fault diagnosis. Appl. Math. Comput., 408.","DOI":"10.1016\/j.amc.2021.126318"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, B., Xu, S., Lei, Y., Leu, C.G., Stewart, E., and Roberts, C. (2022). Multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults. Mech. Syst. Signal Process., 162.","DOI":"10.1016\/j.ymssp.2021.108095"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kim, S., Choi, Y.Y., Kim, K.J., and Choi, J.L. (2021). Forecasting state-of-health of lithium-ion batteries using variational long short-term memory with transfer learning. J. Energy Storage, 41.","DOI":"10.1016\/j.est.2021.102893"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pan, D., Li, H., and Wang, S. (2022). Transfer learning-based hybrid remaining useful life prediction for lithium-ion batteries under different stresses. IEEE Trans. Instrum. Meas., 71.","DOI":"10.1109\/TIM.2022.3142757"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhan, Z., Jiang, P., Sun, Y., Liao, L., Wan, X., Du, Q., Chen, X., Song, H., and Zhu, R. (2022). Whole life cycle performance degradation test and RUL prediction research of fuel cell MEA. Appl. Energy, 310.","DOI":"10.1016\/j.apenergy.2022.118556"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1007\/s00170-021-06780-6","article-title":"Tool wear state prediction based on feature-based transfer learning","volume":"113","author":"Li","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ding, Y., Jia, M., Miao, Q., and Huang, P. (2021). Remaining useful life estimation using deep metric transfer learning for kernel regression. Reliab. Eng. Syst. Saf., 212.","DOI":"10.1016\/j.ress.2021.107583"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ding, Y., Ding, P., and Jia, M. (2021). A novel remaining useful life prediction method of rolling bearings based on deep transfer auto-encoder. IEEE Trans. Instrum. Meas., 70.","DOI":"10.1109\/TIM.2021.3072670"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1109\/TMECH.2021.3094986","article-title":"A new intermediate domain SVM-based transfer model for rolling bearing RUL prediction","volume":"27","author":"Shen","year":"2021","journal-title":"IEEE ASME Trans. Mechatron."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mao, W., Liu, J., Chen, J., and Liang, X. (2022). An interpretable deep transfer learning-based remaining useful life prediction approach for bearings with selective degradation knowledge fusion. IEEE Trans. Instrum. Meas., 71.","DOI":"10.1109\/TIM.2022.3159010"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1109\/TII.2021.3081595","article-title":"Fault knowledge transfer assisted ensemble method for remaining useful life prediction","volume":"18","author":"Xia","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cheng, H., Kong, X., Wang, Q., Ma, H., and Yang, S. (2022). The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data. Reliab. Eng. Syst. Saf., 225.","DOI":"10.1016\/j.ress.2022.108581"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Jia, M., Ding, Y., and Ding, P. (2021). Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors. Reliab. Eng. Syst. Saf., 216.","DOI":"10.1016\/j.ress.2021.107946"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Miao, M., Yu, J., and Zhao, Z. (2022). A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions. Reliab. Eng. Syst. Saf., 219.","DOI":"10.1016\/j.ress.2021.108259"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Miao, M., and Yu, J. (2021). A deep domain adaptative network for remaining useful life prediction of machines under different working conditions and fault modes. IEEE Trans. Instrum. Meas., 70.","DOI":"10.1109\/TIM.2021.3084305"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, X., Li, J., Zuo, L., Zhu, L., and Shen, H.T. (2022). Domain adaptive remaining useful life prediction with transformer. IEEE Trans. Instrum. Meas., 71.","DOI":"10.1109\/TIM.2022.3200667"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fan, Y., Nowaczyk, S., and R\u00f6gnvaldsson, T. (2020). Transfer learning for remaining useful life prediction based on consensus self-organizing models. Reliab. Eng. Syst. Saf., 203.","DOI":"10.1016\/j.ress.2020.107098"},{"key":"ref_29","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2021, January 2\u20139). Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., and Eklund, N. (2008, January 6). Damage propagation modeling for aircraft engine run-to-failure simulation. Proceedings of the 2008 International Conference on Prognostics and Health Management, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ress.2012.03.008","article-title":"Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life","volume":"103","author":"Hu","year":"2012","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_33","first-page":"1","article-title":"Performance benchmarking and analysis of prognostic methods for CMAPSS datasets","volume":"5","author":"Ramasso","year":"2014","journal-title":"Int. J. Progn. Health. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ma, J., Su, H., Zhao, W.-L., and Liu, B. (2018). Predicting the remaining useful life of an aircraft engine using a stacked sparse autoencoder with multilayer self-learning. Complexity, 2018.","DOI":"10.1155\/2018\/3813029"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.isatra.2020.06.005","article-title":"Degradation prognosis for proton exchange membrane fuel cell based on hybrid transfer learning and intercell differences","volume":"113","author":"Ma","year":"2021","journal-title":"ISA Trans."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.isatra.2019.07.004","article-title":"Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network","volume":"97","author":"Wu","year":"2020","journal-title":"ISA Trans."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.neucom.2018.09.076","article-title":"Bidirectional handshaking LSTM for remaining useful life prediction","volume":"323","author":"Elsheikh","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kong, Z., Cui, Y., Xia, Z., and Lv, H. (2019). Convolution and long short-term memory hybrid deep neural networks for remaining useful life prognostics. Appl. Sci., 9.","DOI":"10.3390\/app9194156"}],"container-title":["Axioms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/10\/963\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:05:47Z","timestamp":1760130347000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/10\/963"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,12]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["axioms12100963"],"URL":"https:\/\/doi.org\/10.3390\/axioms12100963","relation":{},"ISSN":["2075-1680"],"issn-type":[{"value":"2075-1680","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,12]]}}}