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首页> 外文会议>Annual Symposium on Quantitative Nondestructive Evaluation >AN ADAPTIVE CLASSIFIER FOR DETECTING HELICOPTER DRIVETRAIN DAMAGE USING ACOUSTIC EMISSION
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AN ADAPTIVE CLASSIFIER FOR DETECTING HELICOPTER DRIVETRAIN DAMAGE USING ACOUSTIC EMISSION

机译：一种用于检测使用声发射的直升机动力损伤的自适应分类器

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摘要

This paper describes recent developments in a program to detect damage to helicopter drivetrains using acoustic emission (AE). Data obtained from an SH-60 drivetrain on an NAWC test stand was correlated with seeded fault damage in order to identify acoustic emission characteristics unique to the various sources. The objective is to extend prior work in applications of pattern recognition techniques and advanced machine intelligence to AE by designing and implementing an autonomous adaptive procedure to recognize and classify drivetrain damage from AE data. Our approach was evaluate time series and frequency features of AE waveforms for evidence of damage-related effects, bound the feature values associated with baseline data, and finally take advantage of an unexpected, but not surprising, characteristic associated with damage of increasing severity. In essence we detect damage by identifying signals whose feature values exceed the baseline limits, then calculating a 'distance' to the feature. This distance increases with damage severity so that increasing distance, i.e. having a positive 'velocity', differentiates between damage which is becoming increasingly severe and damage that is stabilized. An example of the former is a growing crack, while stable damage may consist of a spall or broken gear tooth.
机译：本文介绍了使用声发射（AE）检测直升机动脉仪损坏的程序中的最新进展。从Nawc测试台上获得的SH-60动力传动系统获得的数据与种子故障损坏相关，以识别各种来源独特的声发射特性。目的是通过设计和实现自主自适应过程来展示模式识别技术和先进机器智能对AE的应用，以便从AE数据识别和分类动力传动系统损坏的应用程序的应用程序和先进的机器智能。我们的方法是评估AE波形的时间序列和频率特征，以获取与损坏相关效果的证据，与基线数据相关的特征值绑定，最后利用与增加严重程度的损坏相关的意外，但不令人惊讶的特征。实质上，我们通过识别特征值超出基线限制的信号来检测损坏，然后将“距离”计算到该功能。该距离随着伤害严重程度而增加，使得增加距离，即具有正面的“速度”，在越来越严重和稳定的损害之间区分损伤。前者的一个例子是不断增长的裂缝，而稳定的损坏可以由壁齿或破碎的齿轮齿组成。

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