A transfer learning approach for remaining useful life prediction subject to hard failure considering within and between population variations

Xinxing Guo, Song Huang, Jianguo Wu and Chao Wang

Reliability Engineering and System Safety, 2025, vol. 261, issue C

Abstract: Accurate prediction of remaining useful life (RUL) of a unit plays a critical role in condition-based maintenance, especially for hard failure cases. In industrial practice, due to differences in units’ types and working environments, there may exist multiple populations, and even within the same population, there are also variations among units. However, existing methods either assume that different units share the same population characteristics and ignore the between-population variations, or solely focus on between-population knowledge transfer while neglecting the within-population variations. To address this issue, this article proposes a transfer learning approach by integrating a Cox Proportional Hazards (PH) model with a Bayesian hierarchical model, which considers both within and between population variations. Specifically, a shared prior distribution is deployed to the parameters of the Cox model in each population, which builds the foundation for transfer learning across different populations. To model within-population variations, a linear mixed-effects model is utilized to represent heterogeneous degradation data of each unit. The effectiveness of the proposed method is demonstrated and compared with various benchmarks through a simulation study and a case study of turbine engines.

Keywords: Bayesian hierarchical model; Hard failure; Cox PH model; Transfer learning; Remaining useful life prediction (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832025003461
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:261:y:2025:i:c:s0951832025003461

DOI: 10.1016/j.ress.2025.111145

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().