With the availability of cheaper multisensor suites, one has access to massive and multidimensional datasets that can and should be used for fault diagnosis. However, from a time, resource, engineering, and computational perspective, it is often cost prohibitive to label all the data streaming into a database in the context of big machinery data, that is, massive multidimensional data. Therefore, this article proposes both a fully unsupervised and a semi-supervised deep learning enabled generative adversarial network-based methodology for fault diagnostics. Two public datasets of vibration data from rolling element bearings are used to evaluate the performance of the proposed methodology for fault diagnostics. The results indicate that the proposed methodology is a promising approach for both unsupervised and semi-supervised fault diagnostics.

中文翻译：

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滚动轴承的深度半监督生成对抗性故障诊断

随着更便宜的多传感器套件的出现，人们可以访问可以而且应该用于故障诊断的海量多维数据集。然而，从时间、资源、工程和计算的角度来看，在大机械数据，即海量多维数据的背景下，将所有数据流标记到数据库中往往成本高得令人望而却步。因此，本文提出了一种完全无监督和半监督的深度学习生成的基于对抗网络的故障诊断方法。来自滚动轴承的两个公共振动数据数据集用于评估所提出的故障诊断方法的性能。结果表明，所提出的方法对于无监督和半监督的故障诊断都是一种很有前途的方法。