多缺失模式下飞机结构疲劳试验数据的智能补全OA

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.