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To address the insufficient fitting accuracy of late-stage equipment degradation features and excessive prediction errors of remaining useful life(RUL)under existing data-model linkage frameworks,a novel improved RUL prediction method incorporating sample contribution differences was proposed. Multiple weight functions were embedded to revise the conventional objective function, assigning a higher priority to late-stage prediction performance during model optimization. In the offline training phase, multisensor monitoring data were first integrated into composite health indicators using multidimensional fusion coefficients. Second, given the nonlinear characteristics of equipment degradation, an evolution model of composite health indicators was established, and the analytical formula of the RUL was derived based on the first-passage time theory. Finally, an objective function combining the prediction bias and variance was constructed to obtain the optimal fusion coefficients and failure thresholds. In the online prediction phase,sequential Bayesian estimation was utilized for iterative model parameter updating, therby enabling realtime RUL prediction. Comparative validation experiments conducted on aircraft engine cases demonstrate that, compared to traditional data-model linkage RUL prediction methods, the proposed approach reduces the root mean square error by 32.8% and maintains an average relative error within 30% across the entire life cycle. It achieves higher accuracy in late-stage RUL predictions, providing a valuable reference for fault diagnosis and life prediction of industrial equipment, such as aircraft engines.
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Basic Information:
DOI：10.20189/j.cnki.CN/61-1527/E.202603007
China Classification Code:TH17
Citation Information:
[1]LUO Zhengyang,PEI Hong,ZHENG Jianfei ,et al.Remaining Useful Life Prediction Method Incorporating Sample Contribution Difference under Data-Model Linkage Framework[J].Journal of Rocket Force University of Engineering,2026,40(03):89-98.DOI:10.20189/j.cnki.CN/61-1527/E.202603007.
Fund Information:
国家自然科学基金(62227814,62233017,62373368,62373369); 中国博士后科学基金(2023M734286); 陕西省科协青年人才托举计划(20230127); 陕西省军民融合英才支持项目青年拔尖人才计划(2025034)
2026-03-31
2026
2026-04-27
2026-05-18
2026
1
2026-06-15
2026-06-15