The paper presents an innovative approach for structural health monitoring of metallic components under fatigue crack phenomena. The methodology is based on the evaluation of the information entropy of the acoustic emission (AE) data. AE testing of fatigue crack growth (FCG) is performed on metallic components is performed within an extremely noisy testing environment. Basic AE data analysis is demonstrated to be inefficient with regard to the specific testing conditions. AE entropy is proven to be a reliable damage‐sensitive feature for real‐time assessment despite both significant noise disturbance and complexity/randomness of the acoustic phenomena. This was also confirmed for (time‐)discontinuous monitoring processes over random‐based data detections. An innovative monitoring protocol is finally developed according to the experimental evidence also considering the recommendations of the current monitoring. The protocol is found to be promising for structural health monitoring of metallic fracture‐critical components of structures under fatigue.

中文翻译：

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声发射熵：一种用于疲劳载荷下的断裂关键金属部件的结构健康监测的创新方法

本文提出了一种创新方法，用于在疲劳裂纹现象下监测金属部件的结构健康。该方法基于对声发射（AE）数据的信息熵的评估。对金属部件执行疲劳裂纹扩展（FCG）的AE测试是在非常嘈杂的测试环境中进行的。基本的AE数据分析在特定的测试条件下被证明是无效的。尽管有明显的噪声干扰和声学现象的复杂性/随机性，但AE熵已被证明是一种可靠的损伤敏感特性，可用于实时评估。对于基于随机数据检测的（时间）不连续监视过程也得到了确认。最终根据实验证据开发了一种创新的监测方案，同时考虑了当前监测的建议。该协议被发现对于疲劳下结构的金属断裂关键部件的结构健康监测很有希望。