RESEARCH PAPER
Remaining Useful Life Prediction based on Multisource Domain Transfer and Unsupervised Alignment
1
School of Computer, University of Electronic Science and Technology of China, Zhongshan Institute, China
2
School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
3
Guangdong University of Technology, China
These authors had equal contribution to this work
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2024-05-13
Final revision date: 2024-08-13
Acceptance date: 2024-10-04
Online publication date: 2024-11-14
Publication date: 2024-11-14
Corresponding author
Yi lv
School of Computer, University of Electronic Science and Technology of China, Zhongshan Institute, China
School of Computer, University of Electronic Science and Technology of China, Zhongshan Institute, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):194116
HIGHLIGHTS
- A generalizable model based on multisource domain transfer RUL prediction is proposed.
- An adaptive alignment mechanism is proposed for feature alignment.
- The prediction model combines the TCN and DANN to make full use of the timing information in the degradation data.
KEYWORDS
remaining useful life predictionmultisource domain adaptationtemporal conventional networkmultilinear conditioning
TOPICS
ABSTRACT
Transfer learning （TL） enhances remaining useful life (RUL) predictions by addressing data scarcity and operational challenges. Nonetheless, when a significant disparity in degradation data distribution exists between source and target domains, single-source domain TL may lead to misleading or negative transfer. Multisource domain TL partially mitigates these issues but fails to account for substantial discrepancies in feature-label correlations, impairing RUL prediction accuracy. To cope with this problem, we propose a multisource domain unsupervised adaptive learning method powered by a temporal convolutional network. Using a multilinear conditioning strategy, we combine degradation data and subregion labels to construct input characteristics for the domain discriminator. Additionally, we design a feature extractor that produces label-related features invariant across domains, thereby enhancing prediction precision. We evaluate our method using the publicly available C-MAPSS degradation dataset, demonstrating its effectiveness through a case study and ablation experiments.
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