Journal article
A transfer learning approach for remaining useful life prediction subject to hard failure considering within and between population variations
Reliability engineering & system safety, Vol.261, 111145
09/2025
DOI: 10.1016/j.ress.2025.111145
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.
•Both within and between population variations are considered and well characterized.•It enables knowledge transfer from source populations to the new target one.•It can achieve parameter update at both the population and individual level.
Details
- Title: Subtitle
- A transfer learning approach for remaining useful life prediction subject to hard failure considering within and between population variations
- Creators
- Xinxing Guo - Peking UniversitySong Huang - Peking UniversityJianguo Wu - Peking UniversityChao Wang - Department of Industrial and Systems Engineering, University of Iowa, USA
- Resource Type
- Journal article
- Publication Details
- Reliability engineering & system safety, Vol.261, 111145
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.ress.2025.111145
- ISSN
- 0951-8320
- eISSN
- 1879-0836
- Grant note
- National Natural Science Foundation of China: 72171003, 12288101
This work was funded by the National Natural Science Foundation of China (Grant No 72171003 and 12288101) .
- Language
- English
- Date published
- 09/2025
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984816009002771
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