一种用于跨域轴承故障诊断的深度自适应网络

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (3) : 45-53.

论文

夏懿1，2，徐文学1

作者信息 +

A deep adaptive network for cross-domain bearing fault diagnosis

XIA Yi1,2, XU Wenxue1

Author information +

文章历史 +

摘要

针对轴承在不同工况条件下的振动数据存在分布差异，导致诊断精度低的问题，提出一种新的深度自适应网络用于跨域条件下的轴承故障诊断。采用傅里叶变换将原始时域振动信号转换为频域信号并通过深度特征提取器提取其分类特征；利用最大均值差异(Maximize Mean Discrepancy：MMD)来进行深度特征的边缘分布对齐；利用Wasserstein度量网络将源域中有标签数据的类别结构与目标域中无标签数据的类别结构进行匹配，也即对齐不同域的类别条件分布，使得故障数据在不同域的分布能够更好的对齐，从而提高模型在目标域未标签数据集上的分类准确率。实验利用凯斯西储大学公开的故障轴承数据集进行了两种跨域条件的模型迁移，验证了该网络在不同迁移场景中都具有较高的准确率，且优于其它深度自适应网络。

Abstract

The vibration data of bearings are often distributed differently in different working conditions, leading to low fault diagnosis accuracy. To this end, a novel deep adaptation network was proposed for bearing fault diagnosis under cross-domain conditions. First, Fourier transform is used to transform the original vibration signals in the time domain into corresponding signals in the frequency domain. After that, the classification features are extracted by deep feature extractor. Second, Maximize Mean Discrepancy (MMD) is used for aligning marginal distribution of a deep characteristics. Finally, Wasserstein metric network is used to match the category structure of labeled data of the source domain with that of unlabeled data of the target domain, that is, to align the category condition distribution of different domains. By this way, the distribution of data feature can be best aligned in different domains resulting in better diagnosis accuracy of the trained model in the unlabeled target domain. In the experiment, the model migrations under two kinds of cross-domain conditions are designed and tested by using the faulty bearing dataset published by Case Western Reserve University, and it was verified that the network had high diagnosis accuracy in different migration scenarios and was superior to other deep adaptation networks.

导出引用

夏懿1，2，徐文学1. 一种用于跨域轴承故障诊断的深度自适应网络[J]. 振动与冲击, 2022, 41(3): 45-53

XIA Yi1,2, XU Wenxue1. A deep adaptive network for cross-domain bearing fault diagnosis[J]. Journal of Vibration and Shock, 2022, 41(3): 45-53

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