| 一种频域特征提取自编码器及其在故障诊断中的应用研究 |
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| 引用本文： | 赵志宏,李乐豪,杨绍普,李晴.一种频域特征提取自编码器及其在故障诊断中的应用研究[J].中国机械工程,2021,32(20):2468-2474. |
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| 作者姓名： | 赵志宏 李乐豪 杨绍普 李晴 |
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| 作者单位： | 1.省部共建交通工程结构力学行为与系统安全国家重点实验室，石家庄，050043
2.石家庄铁道大学信息科学与技术学院，石家庄，050043 |
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| 基金项目： | 国家自然科学基金(11972236，11790282) |
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| 摘 要： | 提出一种可以直接从振动信号中提取频域特征的非对称自编码器方法。与传统自编码器以重构振动信号作为目标输出不同，频域自编码器使用振动信号的频谱作为目标输出，这种非对称的自编码器可以学习振动信号与其频谱之间的映射关系，使得编码器可以输出频域特征。为了说明提出的频域自编码器的特征提取效果，在轴承数据集上进行特征提取和故障诊断实验，在没有引入标签信息的情况下，频域自编码器提取到的特征表现出较好的聚类效果，能够区分轴承的不同故障类型；进一步进行了泛化实验，训练分类器时使用1%的有标签样本，可以达到90%以上的故障分类准确率。实验结果表明，频域自编码器与传统自编码器相比，可以更好地提取振动信号的故障特征信息，具有一定的实用价值。
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| 关 键 词： | 特征提取 故障诊断 降噪自编码器 深度学习 |
A Frequency Domain Feature Extraction Auto-encoder and Its Applications on Fault Diagnosis |
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| ZHAO Zhihong,LI Lehao,YANG Shaopu,LI Qing.A Frequency Domain Feature Extraction Auto-encoder and Its Applications on Fault Diagnosis[J].China Mechanical Engineering,2021,32(20):2468-2474. |
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| Authors: | ZHAO Zhihong LI Lehao YANG Shaopu LI Qing |
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| Affiliation: | 1.State Key Laboratory of Mechanical Behavior in Traffic Engineering Structure and System Safety,Shijiazhuang,050043
2.School of Computation and Informatics,Shijiazhuang Railway Institute,Shijiazhuang,050043 |
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| Abstract: | An asymmetric frequency domain auto-encoder method was proposed to extract spectrum characteristics of vibration signals. Unlike traditional auto-encoder, which took reconstructed vibration signals as the target output, the frequency domain auto-encoder used the frequency spectrum of vibration signals as the target output. This asymmetric auto-encoder might learn the mapping between vibration signals and the frequency spectrum, so that the encoder might output frequency domain features. In order to illustrate the feature extraction effectiveness of the proposed frequency domain auto-encoder, feature extraction and fault diagnosis experiments were carried out on bearing data sets. Without introducing label information, the features extracted by the frequency domain auto-encoder show better clustering effectiveness and may distinguish different fault types of the bearings. In the further generalization experiment, using 1% labeled samples in training classifier may achieve more than 90% of fault classification accuracy. The experimental results show that the frequency domain auto-encoder may extract fault feature information from vibration signals better than that of the traditional auto-encoder and has certain practical value. |
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| Keywords: | feature extraction fault diagnosis denoising auto-encoder deep learning |
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