Integrating Frequency Guidance into Multi-source Domain Generalization for Bearing Fault Diagnosis

TL;DR

This paper introduces FARNet, a novel Fourier-based data augmentation network that improves bearing fault diagnosis across unseen domains by leveraging frequency domain features and a manifold triplet loss.

Contribution

FARNet uniquely combines amplitude and phase spectrum analysis with a multi-source augmentation strategy and a manifold triplet loss for enhanced domain generalization in fault diagnosis.

Findings

FARNet outperforms existing cross-domain methods on CWRU and SJTU datasets.

Frequency domain augmentation improves model robustness to unseen domains.

Manifold triplet loss enhances decision boundary refinement.

Abstract

Recent generalizable fault diagnosis researches have effectively tackled the distributional shift between unseen working conditions. Most of them mainly focus on learning domain-invariant representation through feature-level methods. However, the increasing numbers of unseen domains may lead to domain-invariant features contain instance-level spurious correlations, which impact the previous models' generalizable ability. To address the limitations, we propose the Fourier-based Augmentation Reconstruction Network, namely FARNet.The methods are motivated by the observation that the Fourier phase component and amplitude component preserve different semantic information of the signals, which can be employed in domain augmentation techniques. The network comprises an amplitude spectrum sub-network and a phase spectrum sub-network, sequentially reducing the discrepancy between the source and target domains. To construct a more robust generalized model, we employ a multi-source domain data augmentation strategy in the frequency domain. Specifically, a Frequency-Spatial Interaction Module (FSIM) is introduced to handle global information and local spatial features, promoting representation learning between the two sub-networks. To refine the decision boundary of our model output compared to conventional triplet loss, we propose a manifold triplet loss to contribute to generalization. Through extensive experiments on the CWRU and SJTU datasets, FARNet demonstrates effective performance and achieves superior results compared to current cross-domain approaches on the benchmarks.