NEWS ARTICLE
学术动态：Reliability-Guided Adaptive Feature Fusion Network for Noise-Robust Bearing Fault Diagnosis
论文标题：Reliability-Guided Adaptive Feature Fusion Network for Noise-Robust Bearing Fault Diagnosis
发布日期：2026-05-22
作者：Song Yang, Mei Liu, Yukang Chen, Jianfeng Zhang, Peng Wang
DOI：10.3390/s26113288
论文摘要：Cross-noise fault diagnosis remains challenging due to the mismatch between training and testing noise conditions, which degrades feature reliability and model generalization. To address this issue, this paper proposes a reliability-guided adaptive feature fusion framework (RGAF-Net). The method focuses on sample-wise adaptive feature fusion, where the enhanced wide first-layer convolutional neural network(WDCNN) backbone is employed to improve multi-scale feature extraction under noisy environments. In addition, a dual-path architecture is introduced to provide complementary representations, including globally robust structural representations and locally detail-sensitive structural responses. Furthermore, a lightweight reliability estimation module is designed to characterize the signal degradation tendency under noisy conditions of each input sample, based on which a sample-wise routing mechanism dynamically adjusts feature contributions during feature fusion. Experiments on two public bearing datasets (PU and JNU) under cross-noise settings demonstrate that the proposed method achieves improved performance compared with representative approaches, particularly under severe noise conditions. For example, on the JNU dataset at −10 dB, the proposed method improves the Macro-F1 score by over 19 percentage points compared with the baseline WDCNN. Ablation studies and visualization analyses further demonstrate the effectiveness and adaptive fusion behavior of the proposed framework. The results indicate that the proposed method provides an effective solution for robust fault diagnosis under noise mismatch scenarios.
元数据：Crossref 收录的 MDPI Sensors 论文。 DOI: 10.3390/s26113288. Vol. 26, Issue 11. Authors: Song Yang, Mei Liu, Yukang Chen, Jianfeng Zhang, Peng Wang.
开放许可：https://creativecommons.org/licenses/by/4.0/
原文链接：https://doi.org/10.3390/s26113288
PDF 链接：https://www.mdpi.com/1424-8220/26/11/3288/pdf