It has been demonstrated that acoustic-emission (AE), inspection of structures can offer advantages over other types of monitoring techniques in the detection of damage; namely, an increased sensitivity to damage, as well as an ability to localise its source. There are, however, numerous challenges associated with the analysis of AE data. One issue is the high sampling frequencies required to capture AE activity. In just a few seconds, a recording can generate very high volumes of data, of which a significant portion may be of little interest for analysis. Identifying the individual AE events in a recorded time-series is therefore a necessary procedure to reduce the size of the dataset. Another challenge that is also generally encountered in practice, is determining the sources of AE, which is an important exercise if one wishes to enhance the quality of the diagnostic scheme. In this paper, a state-of-the-art technique is presented that can automatically identify AE events, and simultaneously help in their characterisation from a probabilistic perspective. A nonparametric Bayesian approach, based on the Dirichlet process (DP), is employed to overcome some of the challenges associated with these tasks. Two main sets of AE data are considered in this work: (1) from a journal bearing in operation, and (2) from an Airbus A320 main landing gear subjected to fatigue testing.

翻译：研究表明，声发射（AE）检测技术在损伤探测方面相较于其他监测手段具有显著优势：其对损伤具有更高的灵敏度，并能定位损伤源。然而，声发射数据分析仍面临诸多挑战。首要问题在于捕捉AE活动所需的高采样频率——短短数秒内产生的海量数据中，大部分可能对分析无实际价值。因此，从记录的时间序列中识别单个AE事件成为压缩数据量的必要步骤。另一个常见实践难题是确定AE源，这对提升诊断方案质量至关重要。本文提出一种先进技术，可从概率视角自动识别AE事件并辅助其特征表征。研究采用基于狄利克雷过程（DP）的非参数贝叶斯方法，以应对上述挑战。本研究主要分析两组AE数据：（1）运行中的滑动轴承数据；（2）经历疲劳测试的空客A320主起落架数据。