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A new transferable bearing fault diagnosis approach with adaptive manifold embedded distribution alignment

机译：一种新的可转移轴承故障诊断方法，具有自适应歧管嵌入式分布对准

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摘要

The conditional and marginal distributions of bearing vibration signal data collected under different working conditions generally may not obey the same distribution. Moreover, feature distortions are difficult to eliminate when performing distribution alignment in the original space. Based on these challenges, this study proposes a novel transferable fault diagnosis approach with adaptive manifold embedded distribution alignment (AMEDA). AMEDA can learn transformed feature representations in the Grassmann manifold space by constructing a geodesic flow kernel to avoid feature distortions. In addition, this paper initially aims to construct an adaptive factor defined by A-distance to adjust the relative importance of the conditional and marginal distributions dynamically. After manifold feature learning and dynamic distribution alignment, a cross-domain classifier f with structural risk minimization can be learned to predict unlabeled target domain data. Experimental results from multiple indicators based on two datasets demonstrate the superiority of AMEDA over the other existing methods.
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