ConvNeXt-FECAM with domain adaptation for rolling bearing cross-condition fault diagnosis

Abstract

To tackle the challenge of insufficient diagnostic accuracy for rolling bearings under cross conditions, this paper proposes a fault diagnosis method based on a ConvNeXt-FECAM architecture, integrated within an enhanced conditional adversarial domain adaptation framework. Specifically, each one-dimensional vibration signal is first reshaped into a two-dimensional (2D) matrix, and a 2D fast Fourier transform magnitude transform is then applied to construct a frequency-enhanced input representation for the ConvNeXt-FECAM feature extractor. In the feature extraction stage, FECAM is embedded into ConvNeXt blocks to assign attention weights to learned feature channels, allowing the network to emphasize feature responses that are more useful for fault-related representation. To further alleviate domain shifts arising from varying operating conditions, a conditional-marginal joint domain alignment strategy is introduced by combining conditional adversarial training with multi-kernel maximum mean discrepancy. Extensive experiments on three rolling bearing datasets demonstrate that the proposed method achieves higher diagnostic accuracy and better transfer performance than representative comparison methods under target operating conditions. Furthermore, ablation studies confirm the contribution of the main components in the proposed framework. The results indicate that the proposed approach has potential for cross-condition fault diagnosis and condition monitoring of rotating machinery.