With the development of artificial intelligence in recent years, deep learning has been widely used in mechanical system signal classification but the impact of different feature extractions on the efficiency and effectiveness of deep learning neural networks is more important. In this study, a vehicle classification based on engine acoustic emission signal in the time domain, the frequency domain and the wavelet transform domain for deep learning network techniques is presented and compared. In signal classification, different feature extractions will show in different decomposition levels and can be used to recognize the various acoustic conditions. In the experimental work, as engines from 10 different ground vehicles operate, the measured sound signal is converted into a digital signal, and the established data set is classified and identified by the deep learning method. The number of samples, identification rate and identification time in the various signal domains are compared and discussed in this study. Finally, the experimental results and data analysis show that by using the wavelet signal and the deep learning method, excellent identification time and identification rate can be achieved, compared with traditional time and frequency domain signals.

中文翻译：

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使用特征分析和深度学习神经网络的声发射信号分类

近年来随着人工智能的发展，深度学习在机械系统信号分类中得到了广泛的应用，但不同特征提取对深度学习神经网络效率和有效性的影响更为重要。在这项研究中，提出并比较了一种基于发动机声发射信号在时域、频域和小波变换域中的深度学习网络技术的车辆分类。在信号分类中，不同的特征提取将表现在不同的分解层次上，可用于识别各种声学条件。在实验工作中，当来自 10 辆不同地面车辆的发动机运转时，将测量到的声音信号转换为数字信号，通过深度学习方法对建立的数据集进行分类识别。本研究对不同信号域中的样本数量、识别率和识别时间进行了比较和讨论。最后，实验结果和数据分析表明，与传统的时域和频域信号相比，使用小波信号和深度学习方法，可以实现优异的识别时间和识别率。