Yasenjiang Jarula, Zhao Yingjun, Xiao Yang, Hao Hebo, Gong Zhichao, Han Shuaihua
College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China.
Sensors (Basel). 2025 Jun 21;25(13):3871. doi: 10.3390/s25133871.
As the most basic mechanical components, bearing troubleshooting is essential to ensure the safe and reliable operation of rotating machinery. Bearing fault diagnosis is challenging due to the scarcity of bearing fault diagnosis samples and the susceptibility of fault signals to external noise. To address these issues, a ResNet-CACNN-BiGRU-SDPA bearing fault diagnosis method based on time-frequency bi-domain and feature fusion is proposed. First, the model takes the augmented time-domain signals as inputs and reconstructs them into frequency-domain signals using FFT, which gives the signals a bi-directional time-frequency domain receptive field. Second, the long sequence time-domain signal is processed by a ResNet residual block structure, and a CACNN method is proposed to realize local feature extraction of the frequency-domain signal. Then, the extracted time-frequency domain long sequence features are fed into a two-layer BiGRU for bidirectional deep global feature mining. Finally, the long-range feature dependencies are dynamically captured by SDPA, while the global dual-domain features are spliced and passed into Softmax to obtain the model output. In order to verify the model performance, experiments were carried out on the CWRU and JNU bearing datasets, and the results showed that the method had high accuracy under both small sample size and noise perturbation conditions, which verified the model's good fault-feature-learning capability and noise immunity performance.
作为最基本的机械部件，轴承故障排查对于确保旋转机械的安全可靠运行至关重要。由于轴承故障诊断样本稀缺以及故障信号易受外部噪声影响，轴承故障诊断具有挑战性。为解决这些问题，提出了一种基于时频双域和特征融合的ResNet-CACNN-BiGRU-SDPA轴承故障诊断方法。首先，该模型将增强后的时域信号作为输入，并使用快速傅里叶变换（FFT）将其重构为频域信号，这赋予了信号双向时频域感受野。其次，通过ResNet残差块结构处理长序列时域信号，并提出一种卷积注意力通道神经网络（CACNN）方法来实现频域信号的局部特征提取。然后，将提取的时频域长序列特征输入到两层双向门控循环单元（BiGRU）中进行双向深度全局特征挖掘。最后，通过自注意力动态池化（SDPA）动态捕获长程特征依赖关系，同时将全局双域特征拼接并传入Softmax以获得模型输出。为验证模型性能，在西储大学（CWRU）和江南大学（JNU）轴承数据集上进行了实验，结果表明该方法在小样本量和噪声干扰条件下均具有较高的准确率，验证了模型良好的故障特征学习能力和抗噪声性能。
