Feng Tongshuhao, Wang Zhuoran, Qiu Lipeng, Li Hongkun, Wang Zhen
School of Mechanical Engineering, Dalian University, Dalian 116622, China.
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2025 Jun 30;25(13):4091. doi: 10.3390/s25134091.
As a core component of rotating machinery, the condition of rolling bearings is directly related to the reliability and safety of equipment operation; therefore, the accurate and reliable monitoring of bearing operating status is critical. However, when dealing with non-stationary and noisy vibration signals, traditional fault diagnosis methods are often constrained by limited feature characterization from single time-frequency analysis and inadequate feature extraction capabilities. To address this issue, this study proposes a lightweight fault diagnosis model (WaveCAResNet) enhanced with multi-source time-frequency features. By fusing complementary time-frequency features derived from continuous wavelet transform, short-time Fourier transform, Hilbert-Huang transform, and Wigner-Ville distribution, the capability to characterize complex fault patterns is significantly improved. Meanwhile, an efficient and lightweight deep learning model (WaveCAResNet) is constructed based on residual networks by integrating multi-scale analysis via a wavelet convolutional layer (WTConv) with the dynamic feature optimization properties of channel-attention-weighted residuals (CAWRs) and the efficient temporal modeling capabilities of weighted residual efficient multi-scale attention (WREMA). Experimental validation indicates that the proposed method achieves higher diagnostic accuracy and robustness than existing mainstream models on typical bearing datasets, and the classification performance of the newly proposed model exceeds that of state-of-the-art bearing fault diagnostic models on the same dataset, even under noisy conditions.
作为旋转机械的核心部件，滚动轴承的状态直接关系到设备运行的可靠性和安全性；因此，对轴承运行状态进行准确可靠的监测至关重要。然而，在处理非平稳和有噪声的振动信号时，传统故障诊断方法往往受到单一时频分析特征表征有限和特征提取能力不足的限制。为了解决这个问题，本研究提出了一种基于多源时频特征增强的轻量级故障诊断模型（WaveCAResNet）。通过融合连续小波变换、短时傅里叶变换、希尔伯特-黄变换和维格纳-威利分布导出的互补时频特征，显著提高了表征复杂故障模式的能力。同时，基于残差网络构建了一种高效轻量级的深度学习模型（WaveCAResNet），通过小波卷积层（WTConv）集成多尺度分析，结合通道注意力加权残差（CAWRs）的动态特征优化特性和加权残差高效多尺度注意力（WREMA）的高效时间建模能力。实验验证表明，该方法在典型轴承数据集上比现有主流模型具有更高的诊断准确率和鲁棒性，并且在相同数据集上，即使在有噪声的情况下，新提出模型的分类性能也超过了当前最先进的轴承故障诊断模型。
