{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T23:45:38Z","timestamp":1771458338010,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFB2007804"],"award-info":[{"award-number":["2020YFB2007804"]}]},{"name":"National Key Research and Development Program of China","award":["52275094"],"award-info":[{"award-number":["52275094"]}]},{"name":"National Key Research and Development Program of China","award":["2022ZDJS035"],"award-info":[{"award-number":["2022ZDJS035"]}]},{"name":"National Natural Science Foundation of China","award":["2020YFB2007804"],"award-info":[{"award-number":["2020YFB2007804"]}]},{"name":"National Natural Science Foundation of China","award":["52275094"],"award-info":[{"award-number":["52275094"]}]},{"name":"National Natural Science Foundation of China","award":["2022ZDJS035"],"award-info":[{"award-number":["2022ZDJS035"]}]},{"name":"Key Construction Discipline Research Ability Enhancement Project of Guangdong Province","award":["2020YFB2007804"],"award-info":[{"award-number":["2020YFB2007804"]}]},{"name":"Key Construction Discipline Research Ability Enhancement Project of Guangdong Province","award":["52275094"],"award-info":[{"award-number":["52275094"]}]},{"name":"Key Construction Discipline Research Ability Enhancement Project of Guangdong Province","award":["2022ZDJS035"],"award-info":[{"award-number":["2022ZDJS035"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment\u2019s smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective.<\/jats:p>","DOI":"10.3390\/s23187696","type":"journal-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T10:23:42Z","timestamp":1693995822000},"page":"7696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3709-0567","authenticated-orcid":false,"given":"Lingli","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Foshan University, Foshan 528000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heshan","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Foshan University, Foshan 528000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongguang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hujiao","family":"Tang","sequence":"additional","affiliation":[{"name":"Wafangdian Bearing Co., Ltd., Wafangdian Bearing Industrial Park, Dalian 116300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4962-0249","authenticated-orcid":false,"given":"Xuejun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Foshan University, Foshan 528000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianbin","family":"Gao","sequence":"additional","affiliation":[{"name":"Chengdu CRRC Electric Motor Co., Ltd., Chengdu 610500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107866","DOI":"10.1016\/j.knosys.2021.107866","article-title":"Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks","volume":"237","author":"Suh","year":"2022","journal-title":"Knowl. 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