Abstract Acoustic emission (AE) based method is a very promising passive measurement technique for detection of faults and incipient damage in in-service structures. Considering the advantage of detecting even the weak acoustic signals emitting from in-service critical infra-systems for characterizing the fault/damages/leakage in the structures, AE technique is considered to be one of the efficient NDT techniques. In the present work, acoustic emission technique has been utilised to detect leakage in the pipelines by systematically analysing the signal parameters. The leakage in the pipeline is simulated by means of pressure release valves provided at identified locations. Leakage detection in the pipe is carried out for different rate of leakage through valve. AE signals are measured from the sensors attached to the pipeline and the measured signals are analysed to extract the leakage sensitive acoustic wave features. The AE features evaluated from the acoustic signals are further processed to identify- and localize-the leakage (varying flow rates) in the pipe. Out of all the AE features, AE counts, cumulative AE energy, and signal strength are found to be very sensitive parameters to indicate the leakage in the pipelines. Further, support vector machine (SVM) learning and Relevance Vector Machine (RVM) pattern recognition algorithms are employed to develop the hyperplanes and to classify the leakage by using binary- and multiclass-classifications. Results of the study clearly showed that the SVM and RVM enabled AE features can effectively be utilised for identification and localization of leakage in the pipelines.

中文翻译：

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机器学习支持的管道泄漏检测声发射技术

摘要 基于声发射 (AE) 的方法是一种非常有前途的被动测量技术，用于检测在役结构中的故障和初期损坏。考虑到检测甚至从使用中的关键基础设施发出的微弱声信号以表征结构中的故障/损坏/泄漏的优势，AE 技术被认为是有效的 NDT 技术之一。在目前的工作中，声发射技术已被用于通过系统地分析信号参数来检测管道中的泄漏。管道中的泄漏是通过在指定位置提供的压力释放阀来模拟的。管道中的泄漏检测是针对不同的阀门泄漏率进行的。AE 信号是从连接在管道上的传感器测量的，并分析测量的信号以提取泄漏敏感的声波特征。根据声学信号评估的 AE 特征进一步处理以识别和定位管道中的泄漏（变化的流速）。在所有 AE 特征中，发现 AE 计数、累积 AE 能量和信号强度是非常敏感的参数，可以指示管道中的泄漏。此外，支持向量机 (SVM) 学习和相关向量机 (RVM) 模式识别算法用于开发超平面并通过使用二分类和多分类对泄漏进行分类。