Fault diagnosis plays an important role in modern rotating machinery. Recently, some deep neural network-based domain adaptation methods have been successfully applied in cross-domain fault diagnosis problems. However, most of these methods only reduce the distribution discrepancy between different domains, without considering the geometry differences and misalignments across different domains. To this end, a new domain adaptation approach based on a deep neural network is proposed for mechanical fault diagnosis. There are three main contributions in total for the proposed approach. (i) ℓ 2,1 -norm based weight regularization is applied to reinforce the representative features of the original data. (ii) Manifold regularization is employed to further exploit the knowledge of marginal distributions and reduce the geometry differences between different domains. (iii) Subspace alignment is induced to reduce the misalignments of different domains. A gear dataset and ...

中文翻译：

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基于深度神经网络和流形正则化的新型无监督领域自适应机械故障诊断

故障诊断在现代旋转机械中起着重要的作用。最近，一些基于深度神经网络的域自适应方法已经成功地应用于跨域故障诊断问题。但是，大多数这些方法仅减小了不同域之间的分布差异，而没有考虑不同域之间的几何差异和未对准。为此，提出了一种基于深度神经网络的新的域自适应方法，用于机械故障诊断。提议的方法总共有三个主要贡献。（i）应用基于2,1范数的权重正则化来增强原始数据的代表性特征。（ii）使用流形正则化来进一步利用边际分布的知识并减少不同域之间的几何差异。（iii）诱导子空间对齐以减少不同域的未对齐。齿轮数据集和...