A transfer regression network-based adaptive calibration method for remaining useful life prediction considering individual discrepancies in the degradation process of machinery

Jiaxian Chen (), Dongpeng Li (), Ruyi Huang (), Zhuyun Chen () and Weihua Li ()
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Jiaxian Chen: South China University of Technology
Dongpeng Li: South China University of Technology
Ruyi Huang: South China University of Technology
Zhuyun Chen: Guangdong Artificial Intelligence and Digital Economy Laboratory
Weihua Li: Guangdong Artificial Intelligence and Digital Economy Laboratory

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 4, No 27, 2767-2783

Abstract: Abstract Transfer learning (TL)-based remaining useful life (RUL) prediction has been extensively studied and plays a crucial role under cross-working conditions. While previous works have made great efforts to realize domain adaptation, existing methods still suffer from two key limitations: (1) Most feature-based TL methods focus on learning shared domain-independent features and fail to capture private domain information. (2) Model-based TL methods typically use all features to pre-train an RUL prediction model without accounting for the negative effect of private features in the source domain to the target domain. To tackle these challenges, a transfer regression network-based adaptive calibration (TRNAC) method is proposed to execute accurate RUL prediction for different machines where shared domain-independent features and private individual features in the target domain are fully considered to enhance the feature representation for RUL prediction. Specifically, the constructed TRNAC model includes a set of feature extractors where one is to learn shared domain-independent features in both domains and another is to extract individual domain features in the target domain, a shared RUL regressor to learn a mapping relationship between the shared features and the RUL values, a domain discriminator to distinguish which domain the feature comes from. Most importantly, an error regressor is customized by designing a dynamic calibration factor to revise the prediction error caused by the shared RUL regressor and achieve accurate prediction. The comprehensive experimental results on the aero-engine dataset and bearing dataset indicate that the proposed method performs better than other state-of-the-art RUL prediction methods.

Keywords: Remaining useful life prediction; Transfer learning; Dynamic calibration; Domain adaptation; Individual discrepancies (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02386-3

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