A Two-Stage Framework for Small-Sample RUL Prediction on Structurally Complex Time-Series Data
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Abstract
Remaining Useful Life prediction for high-value assets, such as aero-engines, presents a formidable challenge, compounded by sample scarcity, complex data structures and knowledge dilution. This paper proposes a two-stage framework designed to decouple representation construction from temporal pattern learning. Stage I mitigates data complexity by transforming multi-phase snapshot streams into standardized cycle-level sequences through hierarchical aggregation. Stage II addresses data scarcity and multi-target prediction using a Multi-task Shared Transformer. Furthermore, the model is optimized via a risk-aligned loss function that penalizes tardy predictions. The effectiveness of the proposed framework was validated by its strong generalization on PHM 2025 Data Challenge dataset, which ultimately secured a first-place result.
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remaining useful life, predictive maintenance, Transformer, prognostics and health management
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