【摘要】 金属多钉连接结构的准确裂纹诊断对于指导飞机结构地面试验和保障在役安全具有重大意义，然而其裂纹长度–导波损伤因子的异方差不确定性将严重影响结构裂纹诊断以及诊断结果的可靠性评估。针对该问题，本文提出了基于分位数回归神经网络（Quantile regression neural network,QRNN）的多钉结构裂纹诊断方法，采用QRNN构建损伤因子和裂纹长度的映射模型，通过中位数实现结构裂纹长度的诊断。进一步结合分位数输出，得到不同裂纹长度下的诊断可靠性。以复杂多层长桁多钉连接结构为研究对象，开展了诊断与可靠性评估验证。结果表明，所提出方法能够实现典型长桁多钉连接区域裂纹的准确诊断，其中蒙皮上损伤诊断均方根误差为1.2 mm，长桁上损伤诊断均方根误差为2.2 mm，并且实现了诊断结果的可靠性评估。更多还原

【Abstract】 Accurate crack diagnosis in multi-fastener metallic structures is critical for instructing aircraft structural ground tests and ensuring in-service safety. However, heteroscedastic uncertainties in the relationship between crack length and guided-wave damage indices severely compromise diagnostic accuracy and reliability assessment. To address this, a multi-fastener-joint crack diagnosis method based on Quantile Regression Neural Network(QRNN) is proposed. The QRNN establishes a mapping model between guided-wave damage index and the crack length, where crack diagnosis result is determined through the median quantile point. Furthermore, by comprehensively leveraging the quantile outputs, the diagnostic reliability across different crack lengths is quantitatively characterized. A complex multi-layer stringer structure with multiple fastener joints was adopted as the testbed to validate the diagnostic capability and reliability assessment. Experimental results indicate that the proposed approach enables precise crack diagnosis in characteristic longeron fastenerjoint areas, exhibiting Root Mean Squared Error(RMSE) 1.2 mm in the skin and RMSE of 2.2 mm in the stringer, with concurrent quantification of diagnostic reliability.更多还原