利用声发射预测燃气轮机叶片疲劳裂纹扩展

Prediction of Fatigue Crack Growth in Gas Turbine Engine Blades Using Acoustic Emission.

作者信息

Zhang Zhiheng, Yang Guoan, Hu Kun

机构信息

College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

出版信息

Sensors (Basel). 2018 Apr 25;18(5):1321. doi: 10.3390/s18051321.

DOI:10.3390/s18051321

PMID:29693556

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5982231/

Abstract

Fatigue failure is the main type of failure that occurs in gas turbine engine blades and an online monitoring method for detecting fatigue cracks in blades is urgently needed. Therefore, in this present study, we propose the use of acoustic emission (AE) monitoring for the online identification of the blade status. Experiments on fatigue crack propagation based on the AE monitoring of gas turbine engine blades and TC11 titanium alloy plates were conducted. The relationship between the cumulative AE hits and the fatigue crack length was established, before a method of using the AE parameters to determine the crack propagation stage was proposed. A method for predicting the degree of crack propagation and residual fatigue life based on the AE energy was obtained. The results provide a new method for the online monitoring of cracks in the gas turbine engine blade.

摘要

疲劳失效是燃气涡轮发动机叶片出现的主要失效类型，因此迫切需要一种用于检测叶片疲劳裂纹的在线监测方法。所以，在本研究中，我们提出使用声发射（AE）监测来在线识别叶片状态。基于燃气涡轮发动机叶片和TC11钛合金板的声发射监测进行了疲劳裂纹扩展实验。建立了累计声发射撞击次数与疲劳裂纹长度之间的关系，在此基础上提出了一种利用声发射参数确定裂纹扩展阶段的方法。得到了一种基于声发射能量预测裂纹扩展程度和剩余疲劳寿命的方法。研究结果为燃气涡轮发动机叶片裂纹的在线监测提供了一种新方法。