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王琳, 贾飞, 胡晓丽. 融合二维频域与改进型ResNet的故障诊断模型[J]. 桂林电子科技大学学报, 2025, 45(1): 1-10. DOI: 10.16725/j.1673-808X.2023180
引用本文: 王琳, 贾飞, 胡晓丽. 融合二维频域与改进型ResNet的故障诊断模型[J]. 桂林电子科技大学学报, 2025, 45(1): 1-10. DOI: 10.16725/j.1673-808X.2023180
WANG Lin, JIA Fei, HU Xiaoli. Fault diagnosis model integrating 2D frequency domain and improved residual networks[J]. Journal of Guilin University of Electronic Technology, 2025, 45(1): 1-10. DOI: 10.16725/j.1673-808X.2023180
Citation: WANG Lin, JIA Fei, HU Xiaoli. Fault diagnosis model integrating 2D frequency domain and improved residual networks[J]. Journal of Guilin University of Electronic Technology, 2025, 45(1): 1-10. DOI: 10.16725/j.1673-808X.2023180

融合二维频域与改进型ResNet的故障诊断模型

Fault diagnosis model integrating 2D frequency domain and improved residual networks

  • 摘要: 因为数据形态的制约,故障数据经快速傅立叶变换后直接使用深度学习模型,往往不能获得足够的数据特征,使得很难进一步提高变工况下故障诊断的准确性和泛化程度。为此,提出一种融合二维频域与改进残差网络的轻量级故障诊断模型。在获取和学习更多特征之前,截取FFT方法转换所得对称频谱的前半段,并在二维面上以行优先方式展开,得到以矩阵表示的频域数据特征信息;然后以ReLU6作为残差网络的激活函数来学习矩阵特征;最后把终态特征信息映射为独热码向量。在凯斯西储大学的数据集上测试表明,该模型与一维诊断模型相比,诊断准确率提高了7.07个百分点;与标准残差网络相比,准确率的波动降低了0.14个百分点,提升了故障预测的准确率和稳定性。

     

    Abstract: Due to the constraints of data format, using deep learning models directly after fast Fourier transform on fault data often fails to obtain sufficient features, making it difficult to further improve the accuracy and generalization of fault diagnosis under variable operating conditions. To this end, a lightweight fault diagnosis model is proposed that integrates two-dimensional frequency domain and improved residual networks. Before acquiring and learning more features, the first half of the symmetric spectrum converted by the FFT method is intercepted and unfolded in a row first manner on a two-dimensional surface to obtain frequency domain data feature information represented by a matrix; Then ReLU6 is used as the Activation function of the improved residual networks to learn the matrix features; Finally, the final state feature information is mapped into a one hot encoding vector. The test on the data set of Case Western Reserve University shows that the diagnostic accuracy of this method is improved by 7.07% compared with the one-dimensional diagnostic model; Compared with the standard residual network, the fluctuation in accuracy has been reduced by 0.14%, improving the accuracy and stability of fault prediction.

     

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