{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T14:28:31Z","timestamp":1767968911820,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["2682024CX018"],"award-info":[{"award-number":["2682024CX018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The remaining useful life (RUL) of complex mechanical systems is the primary aspect of prognostics and health management, which is critical for ensuring reliability and safety. Recent developments have shifted towards a data-driven approach, emphasizing empirical insights over expert opinions. The similarity-based data-driven approach operates on the premise that systems with similar historical behaviors will likely exhibit similar future behaviors, making it suitable for RUL estimation. Conventionally, most similarity-based approaches utilize all historical data to identify reference systems for RUL estimations. However, not all historical events within a system hold equal significance for RUL. Certain events have a substantial impact on the remaining lifespan of a system. These significant and impactful events are called degradation events (DEs) in this study. Based on the hypothesis that systems undergoing similar DEs may share the same RUL, this study presents an innovative framework for RUL estimation that leverages only the DEs of a test system to identify reference systems that have experienced similar DEs. Furthermore, the model incorporates novel strategies for adjusting the RUL of the reference system based on the initial wear and degradation rates, thereby improving estimation accuracy. The effectiveness of the proposed model, in comparison with similar state-of-the-art models, is demonstrated through experiments on widely recognized jet engine datasets provided by NASA and bearing degradation data from the XJTU-SY.<\/jats:p>","DOI":"10.3390\/info16070542","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T05:53:13Z","timestamp":1750917193000},"page":"542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Degradation Events for Enhanced Remaining Useful Life Prediction"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9594-8595","authenticated-orcid":false,"given":"Zeeshan","family":"Abbas","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5121-8320","authenticated-orcid":false,"given":"Muhammad","family":"Sharif","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Musrat","family":"Hussain","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9043-0826","authenticated-orcid":false,"given":"Naeem","family":"Hussain","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2661-206X","authenticated-orcid":false,"given":"Mehboob","family":"Hussain","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naveed Ahmad","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Information Technology and Information Systems, University of Canberra, Canberra, ACT 2617, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9521","DOI":"10.1109\/TIE.2019.2924605","article-title":"Remaining useful life prediction based on a double-convolutional neural network architecture","volume":"66","author":"Yang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103849","DOI":"10.1016\/j.engappai.2020.103849","article-title":"Engineering Applications of Artificial Intelligence Remaining useful life estimation with multiple local similarities","volume":"95","author":"Lyu","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Calabrese, M., Cimmino, M., Fiume, F., Manfrin, M., Romeo, L., Ceccacci, S., Paolanti, M., Toscano, G., Ciandrini, G., and Carrotta, A. (2020). SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0. Information, 11.","DOI":"10.3390\/info11040202"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"32919","DOI":"10.1109\/ACCESS.2023.3263196","article-title":"Remaining Useful Life Estimation in Prognostics Using Deep Reinforcement Learning","volume":"11","author":"Hu","year":"2023","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sol\u00eds-Mart\u00edn, D., Gal\u00e1n-P\u00e1ez, J., and Borrego-D\u00edaz, J. (2023). On the Soundness of XAI in Prognostics and Health Management (PHM). Information, 14.","DOI":"10.20944\/preprints202303.0003.v1"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Arunan, A., Qin, Y., Li, X., and Yuen, C. (2024). A change point detection integrated remaining useful life estimation model under variable operating conditions. Control Eng. Pract., 144.","DOI":"10.1016\/j.conengprac.2023.105840"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Behera, S., Patel, Y.S., Choubey, A., Misra, R., Kanani, C.S., and Sillitti, A. (2019, January 8\u201312). Ensemble trees learning based improved predictive maintenance using IIoT for turbofan engines. Proceedings of the 34th ACM Symposium on Applied Computing, New York, NY, USA.","DOI":"10.1145\/3297280.3297363"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107371","DOI":"10.1016\/j.measurement.2019.107371","article-title":"Extracting degradation trends for roller bearings by using a moving-average stacked auto-encoder and a novel exponential function","volume":"152","author":"Xu","year":"2020","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MCSE.2018.110145829","article-title":"A Degradation Degree Considered Method for Remaining Useful Life Prediction Based on Similarity","volume":"21","author":"Liang","year":"2019","journal-title":"Comput. Sci. Eng."},{"key":"ref_10","first-page":"034","article-title":"Attention and Long Short-Term Memory Network for Remaining Useful Lifetime Predictions of Turbofan Engine Degradation","volume":"10","author":"Akcay","year":"2019","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1109\/TIE.2017.2740856","article-title":"Auxiliary particle filtering-based estimation of remaining useful life of IGBT","volume":"65","author":"Haque","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1109\/TIE.2018.2838078","article-title":"A wiener-process-model-based method for remaining useful life prediction considering unit-to-unit variability","volume":"66","author":"Li","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8792","DOI":"10.1109\/TIE.2019.2891463","article-title":"A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions","volume":"66","author":"Huang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1109\/TTE.2019.2944802","article-title":"Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries","volume":"5","author":"Liu","year":"2019","journal-title":"IEEE Trans. Transp. Electrif."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isatra.2020.09.017","article-title":"Ensemble deep learning with multi-objective optimization for prognosis of rotating machinery","volume":"113","author":"Ma","year":"2021","journal-title":"ISA Trans."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.mex.2019.02.015","article-title":"A neural network framework for similarity-based prognostics","volume":"6","author":"Bektas","year":"2019","journal-title":"MethodsX"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., and Eklund, N. (2008, January 6\u20139). Damage propagation modeling for aircraft engine run-to-failure simulation. Proceedings of the 2008 International Conference on Prognostics and Health Management, PHM 2008, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/TR.2018.2882682","article-title":"A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings","volume":"69","author":"Wang","year":"2020","journal-title":"IEEE Trans. Reliab."},{"key":"ref_19","first-page":"4877","article-title":"Improved trajectory similarity-based approach for turbofan engine prognostics","volume":"33","author":"Huang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ren, Y., Lyu, J., Jebali, M., and Zhang, B. (2023, January 23\u201325). An AC Contactor Remaining Useful Life Prediction Method based on Degradation Event Analysis. Proceedings of the 2023 6th International Symposium on Autonomous Systems (ISAS), Nanjing, China.","DOI":"10.1109\/ISAS59543.2023.10164499"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.jmsy.2020.06.014","article-title":"Anomaly monitoring improves remaining useful life estimation of industrial machinery","volume":"56","author":"Aydemir","year":"2020","journal-title":"J. Manuf. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.isatra.2020.12.020","article-title":"Selection of efficient degradation features for rolling element bearing prognosis using Gaussian Process Regression method","volume":"112","author":"Kumar","year":"2021","journal-title":"ISA Trans."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Biggio, L., and Kastanis, I. (2020). Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead. Front. Artif. Intell., 3.","DOI":"10.3389\/frai.2020.578613"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00170-018-2874-0","article-title":"A neural network filtering approach for similarity-based remaining useful life estimation","volume":"101","author":"Bektas","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1109\/TIE.2020.2972443","article-title":"Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach","volume":"68","author":"Chen","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_26","first-page":"1","article-title":"Investigating computational geometry for failure prognostics","volume":"5","author":"Ramasso","year":"2014","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kang, Z., Catal, C., and Tekinerdogan, B. (2021). Remaining useful life (Rul) prediction of equipment in production lines using artificial neural networks. Sensors, 21.","DOI":"10.3390\/s21030932"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108590","DOI":"10.1016\/j.ress.2022.108590","article-title":"Trend attention fully convolutional network for remaining useful life estimation","volume":"225","author":"Fan","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"129443","DOI":"10.1109\/ACCESS.2022.3226780","article-title":"Data-Efficient Estimation of Remaining Useful Life for Machinery With a Limited Number of Run-to-Failure Training Sequences","volume":"10","author":"Sternharz","year":"2022","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1007\/s12065-022-00805-z","article-title":"Aero engines remaining useful life prediction based on enhanced adaptive guided differential evolution","volume":"17","author":"Abdelghafar","year":"2024","journal-title":"Evol. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, X., Xu, S., Yang, Y., Lin, T., Mba, D., and Li, C. (2024). Spherical-dynamic time warping\u2014A new method for similarity-based remaining useful life prediction. Expert Syst. Appl., 238.","DOI":"10.1016\/j.eswa.2023.121913"},{"key":"ref_32","unstructured":"Wen, Z., Fang, Y., Wei, P., Liu, F., Chen, Z., Member, S., and Wu, M. (2024). Temporal and heterogeneous graph neural network for remaining useful life prediction. arXiv."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/542\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:58:53Z","timestamp":1760032733000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/542"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,26]]},"references-count":32,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["info16070542"],"URL":"https:\/\/doi.org\/10.3390\/info16070542","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,26]]}}}