声发射
光谱图
信号(编程语言)
卷积神经网络
声学
计算机科学
材料科学
人工智能
模式识别(心理学)
物理
程序设计语言
作者
Sarah Ennis,Victor Giurgiutiu
出处
期刊:Journal of nondestructive evaluation, diagnostics and prognostics of engineering systems
[ASME International]
日期:2023-12-11
卷期号:7 (1)
摘要
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 testing of two specimen types. One specimen type had a 1-mm hole for crack initiation. The other specimen type had a 150-µm 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 from both 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.
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