随机载荷谱下基于声发射的耳片接头疲劳裂纹识别方法

作为航空结构中传递集中载荷的关键部件，耳片接头的失效将会带来灾难性的后果，因此需要通过疲劳试验来验证其是否满足设计要求。声发射（AE）作为一种在线监测手段，能够及时捕捉接头裂纹萌生扩展进程，提升试验质量。然而，在随机载荷谱下，会产生大量极其复杂且毫无规律的声发射信号，致使传统的基于特征提取及参数滤波的声发射数据分析方法难以发挥作用，无法有效识别接头裂纹的萌生。因此，本文提出了一种随机载荷谱下疲劳裂纹的声发射识别方法，该方法利用随机载荷谱的分布特点，通过分析不同载荷谱块下相同循环时段所对应的声发射信号表象差异来发现、锁定异常，并结合定位分析与干扰排除分析，确定裂纹的发生时间及位置。通过疲劳试验，该方法的有效性得到了证实，因而可为随机载荷谱下的相关航空结构试验提供参考。

Fatigue damage monitoring of lug joints with random load spectrum based on acoustic emission

As a crucial part in aeronautical structures, the lug joint plays a critical role in transmitting concentrated loads. Its failure is bound to bring catastrophic consequences. Hence, the fatigue performance of the lug needs to be investigated by test. Since the Acoustic Emission (AE) technology is an on-line monitoring method that can capture the crack initiation and propagation process in time, it is chosen to monitor the damage to improve the test quality. However, due to the random load spectrum, a large number of extremely complex, irregular and intense AE noise signals will be generated, hampering the functioning of traditional AE analysis methods based on feature extraction and parameter filtering, and thus unable to effectively identify the initiation of joint cracks. This paper proposes a novel AE identification method of fatigue cracks with random load spectrum, utilizing the distribution characteristics of the random load spectrum. By analyzing the differences of AE signals from different load spectrum blocks, we can find and locate the abnormal signals. Furthermore, location analysis and interference elimination analysis are adopted to ultimately determine the occurrence time and location of the cracks. The validity of this method has been verified by the fatigue test, providing reference for the related aeronautical structure tests with random load spectrum.