Intelligent data imputation for aircraft structural fatigue tests with multiple missing patterns

Full-scale fatigue testing plays a crucial role in verifying aircraft structural design and durability; however, it involves long test cycles, numerous sensors, and complex operating conditions. Multi-channel data loss often occurs due to uncertainties in data acquisition systems and external interference, directly affecting data integrity and the reliability of subsequent evaluations. To address this issue, this study proposes a self-attention-based multi-channel imputation model for full-scale fatigue test data. The proposed model captures both long- and short-term dependencies through a two-layer diagonal masked self-attention mechanism, focusing on intra-channel local details and depicting inter-channel global correlations. It further integrates attention weights with missing masks via a weighted combination strategy to adapt to different missing mechanisms and noise conditions. In terms of learning strategy, a dual-objective joint optimization framework is adopted, combining masked imputation and observed-value reconstruction with a hierarchical weighted loss function. These designs enable the model to balance local imputation accuracy with global temporal consistency, suppress information leakage, and improve robustness. Comparative and ablation experiments were conducted on real full-scale fatigue test data under various missing patterns and missing rates. The results demonstrate that the proposed method is effective and stable, significantly enhances the integrity of fatigue test data, and provides a reliable data foundation for fatigue anomaly detection and life assessment.