ABSTRACT

The problem of evaluating the risk of failure associated with the propagation of a crack in a pipe under pressure has great practical relevance, and it may be tackled with acoustic-emission techniques. Artificial neural networks may be trained to classify the acoustic emissions generated by the crack according to the phase of propagation, and such a classification permits to evaluate the risk of mantaining a system in operation. In order to train the network, a human specialist has to estimate the transition times between any two consecutive phases by inspecting the results of a previous hydrostatic test, and such determination of the transition times has a high degree of subjectivity and uncertainty, affecting the classification performance of the network. In this paper, we propose a human-independent method for the estimation of the transition times, and we show successful applications to the data from two hydrostatic tests. For a test on a 2 m-long pipe, the method exhibited 98% of correct-classification rate, an improvement of 8% over results obtained with human-determined transition times. For a 40 m-long pipe, under experimental conditions comparable to those found in industrial applications, the method exhibited 91% of correct-classification rate. The proposed method provides a fully automated framework for the evaluation of the state of a crack.

摘要

评估与压力下管道中裂纹扩展相关的失效风险的问题具有很大的实际意义，可以通过声发射技术来解决。可以训练人工神经网络以根据传播阶段对裂缝产生的声发射进行分类，并且这样的分类允许评估维持系统运行的风险。为了训练网络，人类专家必须通过检查之前的水压试验结果来估计任意两个连续阶段之间的过渡时间，这种过渡时间的确定具有高度的主观性和不确定性，影响分类网络的性能。在本文中，我们提出了一种独立于人的估计过渡时间的方法，并且我们展示了对来自两个静水压试验数据的成功应用。对于 2 m 长管道的测试，该方法显示出 98% 的正确分类率，比人类确定的过渡时间获得的结果提高了 8%。对于 40 m 长的管道，在与工业应用中发现的实验条件相当的实验条件下，该方法表现出 91% 的正确分类率。所提出的方法为裂纹状态的评估提供了一个完全自动化的框架。对于 40 m 长的管道，在与工业应用中发现的实验条件相当的实验条件下，该方法表现出 91% 的正确分类率。所提出的方法为裂纹状态的评估提供了一个完全自动化的框架。对于 40 m 长的管道，在与工业应用中发现的实验条件相当的实验条件下，该方法表现出 91% 的正确分类率。所提出的方法为裂纹状态的评估提供了一个完全自动化的框架。