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Abstract

Accurate fault diagnosis of rolling bearings under varying operating conditions remains challenging due to distribution shifts in vibration data. While feature-based transfer learning methods mitigate this issue, many suffer from limitations: over-reliance on maximum mean discrepancy with non-characteristic kernels, which may discard critical distribution information, and the assumption of a single shared transformation matrix for domain alignment, which fails under significant distribution shifts. To address these shortcomings, a discriminative geometric distribution alignment (DGDA) model is proposed for cross-domain fault diagnosis. DGDA simultaneously optimizes two distinct transformation matrices for source and target domains. It integrates the dynamic maximum mean discrepancy, maximum covariance discrepancy to establish a refined distribution discrepancy metric, effectively capturing complex data differences. The model further preserves geometric structures by enhancing inter-class separation and intra-class compactness in source domain while maximizing feature variance in target domain. Finally, based on the new feature representations, a pattern recognition method is used to diagnose the target domain samples. The fault diagnosis experiments under variable operating conditions and cross-machine verify that the proposed model can improve the domain adaptation between source and target domains, enhance the transfer ability of source domain diagnosis knowledge, and realize cross-domain fault diagnosis under different data distributions more accurately.

Keywords

discriminative geometric distribution alignment transfer learning fault diagnosis variable conditions

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Cross domain fault diagnosis method based on discriminative geometric distribution alignment

Abstract

Keywords

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References