A Multimodal Lightweight Transformer for Bearing Fault Diagnosis Under High-Noise Industrial IoT Environments

作者：Junjie Liu, Jiaxian Zhu, Weihua Bai, Huibing Zhang, Lianghai Wu, Teng Zhou, Keqin Li · 发表于：IEEE Internet of Things Journal · 年份：2025 · DOI：10.1109/jiot.2025.3634730 · 被引用次数：3 · 研究领域：Machine Fault Diagnosis Techniques、Advanced SAR Imaging Techniques、Sparse and Compressive Sensing Techniques

Industrial IoT (IIoT) sensing nodes for bearing monitoring often operate in high-noise environments, where acoustic and mechanical interference masks weak fault signatures and undermines diagnostic reliability. To address this challenge, we propose a lightweight multi-modal fusion framework for robust fault diagnosis under extreme noise. Our method first applies multi-resolution decomposition with selective reconstruction and adaptive enhancement to preserve fault-related components while suppressing interference. The enhanced signals are transformed from 1D time series into two-dimensional representations to jointly capture temporal dynamics and spectral characteristics. We then design a tri-branch multi-head attention architecture that integrates a multiscale recurrence plot network, a Gramian angular field network, and a lightweight residual network. Learnable attention weights enable adaptive fusion of complementary cross-modal features with low computational overhead. Extensive experiments on the CWRU benchmark show superior robustness from 0 dB to -6 dB SNR, with a mean accuracy above 99.6% and consistent gains over eight state-of-the-art methods. Additional tasks on single-domain diagnosis, cross-condition fault type recognition, and fault degree discrimination (T1–T3) confirm strong generalization and multi-scale adaptability, with average improvements of 2–4% over the second-best baseline and stable variance under noise. The compact architecture and high noise immuni...