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）结构检测相较于其他监测技术在损伤检测方面具有优势，包括对损伤的更高灵敏度以及定位损伤源的能力。然而，声发射数据分析仍面临诸多挑战。首要问题在于捕获声发射活动所需的高采样频率——仅需数秒即可产生海量数据，其中大部分对于分析而言价值有限。因此，在记录的时间序列中识别单个声发射事件成为缩减数据集规模的必要步骤。实践中普遍存在的另一挑战是确定声发射源，这对于提升诊断方案质量至关重要。本文提出了一种先进技术，能够自动识别声发射事件，同时从概率角度辅助其表征。基于狄利克雷过程（DP）的非参数贝叶斯方法被用于克服这些任务中的部分挑战。本研究考虑两类主要声发射数据：（1）运行中滑动轴承的声发射数据，（2）空客A320主起落架疲劳试验的声发射数据。