Wulan Tuoya, Li Guodong, Huo Yupeng, Yu Jiangjiang, Wang Ruiqi, Kou Zhongzheng, Yang Wen
School of Transportation, Inner Mongolia University, Hohhot 010031, China.
Inner Mongolia Engineering Research Center of Testing and Strengthening for Bridges, Hohhot 010020, China.
Sensors (Basel). 2024 Sep 23;24(18):6145. doi: 10.3390/s24186145.
Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials' structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals.
声发射（AE）技术作为一种无损检测方法，被广泛用于监测各种材料的结构完整性。然而，在实验过程中捕获的AE信号常常受到各种噪声因素的影响，这些噪声会降低信号的清晰度和完整性，使得对实验结果进行精确的分析评估变得复杂。针对这些挑战，本文介绍了一种专门为AE信号量身定制的基于无监督深度学习的去噪模型。它将自身的有效性与诸如小波包去噪、希尔伯特变换去噪以及自适应噪声的完备总体经验模态分解等已有的方法进行了对比。结果表明，无监督跳跃自编码器模型在降噪方面展现出巨大潜力，标志着AE信号处理取得了重大进展。随后，本文着重将这种先进的去噪技术应用于在钢纤维增强混凝土（SFRC）拉伸试验、钢材拉伸试验以及钢筋混凝土梁弯曲试验过程中采集的AE信号，并详细讨论了原始信号与降噪后信号在波形和频谱图方面的变化。结果表明，该模型也能够去除AE信号中的噪声。
