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

TL;DR

This document provides a comprehensive overview of the elsarticle.cls LaTeX class, designed for Elsevier submissions and built on article.cls to minimize package conflicts. It contrasts elsarticle.cls with the older elsart.cls, detailing improvements such as preprint formatting, easier theorem/list formatting, and integrated natbib/hyperref support. It also offers practical installation instructions from Elsevier resources and CTAN and describes how to use the class with configurable options to produce publication-ready manuscripts. Overall, it standardizes and simplifies Elsevier-compatible typesetting with robust handling of front matter and layout choices.

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.