AI-enabled crack-length estimation from acoustic emission signal signatures

IF 2 Q2 ENGINEERING, MULTIDISCIPLINARY
Shane Ennis, Victor Giurgiutiu
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引用次数: 0

Abstract

Abstract This article addresses the classification of fatigue crack length using artificial intelligence (AI) applied to acoustic emission (AE) signals. The AE signals were collected during fatigue of two specimen types. One specimen type had a 1-mm hole for crack initiation. The other specimen type had a 150-micron wide slit of various lengths. Fatigue testing was performed under stress-intensity-factor control to moderate crack advancement. The slit specimen produced AE signals only from crack advancement at the slit tips whereas the 1-mm hole specimens produced AE signals both from crack tip advancement and crack rubbing or clapping. The AE signals were captured with a piezoelectric wafer active sensor (PWAS) array connected to MISTRAS instrumentation and AEwin software. The collected AE signals were preprocessed using time-of-flight filtering and denoising. Choi Williams transform converted time-domain AE-signals into spectrograms. To apply machine learning, the spectrogram images were used as input data for the training, validation, and testing of a GoogLeNet convolutional neural network (CNN). The CNN was trained to sort the AE signals into crack-length classes. CNN performance enhancements, including synthetic data generation and class balancing were developed. A three-class example with crack lengths of (i) 10-12 mm; (ii) 12-14 mm; and (iii) 14-16 mm is provided. Our AI approach was able to classify the AE signals into these three classes with 91% accuracy thus proving that the AE signals contain sufficient information for crack estimation using an AI-enabled approach.
基于声发射信号特征的人工智能裂纹长度估计
摘要本文研究了利用人工智能(AI)对声发射信号进行疲劳裂纹长度分类。采集了两种试样疲劳过程中的声发射信号。一种样品类型有一个1毫米的孔,用于裂纹萌生。另一种类型的标本具有150微米宽的不同长度的狭缝。在应力强度因子控制下进行疲劳试验,以调节裂纹的扩展。狭缝试样只产生裂纹尖端推进的声发射信号,而1 mm孔试样同时产生裂纹尖端推进和裂纹摩擦或拍击声发射信号。声发射信号由连接到MISTRAS仪器和AEwin软件的压电片有源传感器阵列捕获。对采集到的声发射信号进行飞行时间滤波和去噪预处理。Choi Williams将转换后的时域ae信号转换成频谱图。为了应用机器学习,频谱图图像被用作GoogLeNet卷积神经网络(CNN)的训练、验证和测试的输入数据。训练CNN将声发射信号分类为裂缝长度类。CNN的性能增强,包括合成数据生成和类平衡。裂隙长度为(i) 10 ~ 12mm的三级实例;(ii) 12-14毫米;(iii)提供14- 16mm。我们的人工智能方法能够以91%的准确率将AE信号分为这三类,从而证明AE信号包含足够的信息,可以使用启用人工智能的方法进行裂缝估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.80
自引率
9.10%
发文量
25
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