Dynamic evolution-driven domain adaptation with wavelet dual-path structure for cross-domain fault diagnosis

Abstract

Traditional fault diagnosis methods often suffer from performance degradation under new working conditions due to distribution shifts between the source and target domains. To bridge this gap in cross-domain fault diagnosis (CDFD), the domain adaptation (DA) technique leverages transfer learning to align feature distributions, which facilitates knowledge transfer from labeled source domains to unlabeled target domains. Although existing studies on DA have demonstrated efficacy, they still face significant challenges due to abrupt domain shifts and insufficient feature discrimination. To overcome these problems, this study proposes a dynamic evolution mechanism to construct a sequence of hybrid domains that gradually evolves from the source to the target domain. This strategy establishes a smooth transition path to mitigate abrupt domain shifts. Additionally, a dual-path feature extraction structure empowered by wavelet packet transform (WPT) is introduced. This structure decomposes input signals into high-frequency and low-frequency components to enhance discriminative feature representation. The experimental results on rolling bearing and gearbox datasets demonstrate the effectiveness and generalization performance of the proposed method.