基于注意力的多尺度残差卷积网络轴承故障诊断
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北京信息科技大学机电工程学院 北京 100192

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TH133.3;TN06

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国家自然科学基金(62303065)项目资助


Attention-based Multi-scale residual convolutional network for bearing fault diagnosis
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School of Electromechanical Engineering, Beijing Information Science and Technology University,Beijing 100192, China

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    摘要:

    针对轴承故障信号中存在复杂特征的特点,提出了一种结合注意力机制与多尺度残差卷积网络轴承故障判定方法。该模型结合了卷积神经网络(CNN)的强大特征提取能力和注意力机制的自适应加权能力,能够有效地处理轴承故障信号中的复杂特征。模型采用了多尺度卷积层,通过不同大小的卷积核捕获信号的多尺度特征,有助于识别不同类型和严重程度的故障。同时,引入残差结构,通过高维与低维特征的协同决策机制,有效整合多层卷积提取的特征,增强了模型对关键信息的感知能力,并降低了深度网络训练中的梯度消失和特征冗余问题,从而保证了模型的稳定性和准确性。注意力机制(如SEBlock和ECABlock)的融合,使模型能够自适应地关注更加重要的特征通道,进一步提升了诊断性能。实验结果表明,该模型在强噪声下能实现高精度的诊断,展示了其在智能维护和故障预警系统中的应用潜力。

    Abstract:

    Aiming at the characteristics of complex features in bearing fault signals, a bearing fault determination method combining the attention mechanism and multi-scale residual convolutional network is proposed. The model combines the powerful feature extraction capability of convolutional neural network (CNN) and the adaptive weighting capability of the attention mechanism, which can effectively deal with the complex features in the bearing fault signal. The model employs a multi-scale convolutional layer, which captures the multi-scale features of the signal through different sizes of convolutional kernels, which helps to recognize different types and severities of faults. Meanwhile, the residual structure is introduced to effectively integrate the features extracted by multilayer convolution through the cooperative decision-making mechanism of high-dimensional and low-dimensional features, which enhances the model′s ability to perceive the key information and reduces the problems of gradient vanishing and feature redundancy in the training of the deep network, so as to ensure the stability and accuracy of the model. The fusion of attention mechanisms (e.g., SEBlock and ECABlock) enables the model to adaptively focus on more important feature channels, which further improves the diagnostic performance. The experimental results show that the model can achieve high-precision diagnosis under various fault modes, demonstrating its potential application in intelligent maintenance and fault warning systems.

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李强,马超,黄民.基于注意力的多尺度残差卷积网络轴承故障诊断[J].电子测量技术,2025,48(9):19-26

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  • 在线发布日期: 2025-05-23
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