基于双线性池化引导特征融合的轴承故障诊断算法

Bilinear Pooling Guided Feature Fusion for Bearing Fault Diagnosis

为了深入优化滚动轴承在变负载驱动环境下特征提取不充分、轴承故障特征表征不足的问题,提出了基于双线性池化引导特征融合的轴承故障诊断算法。对读取到的原始信号数据进行预处理,通过去除直流分量、噪声滤波、抗混叠滤波、时域窗函数等操作,提高信号处理后的振动谱图质量;对预处理后的信号数据进行傅里叶变换,计算出变换后的幅值和频率数据,并绘制对应的振动谱图;利用通道注意力和空间注意力改进Res2Net网络,提取不同关注点下的视觉特征,并基于双线性池化方法进行多特征融合;利用全连接和softmax函数构建分类头,实现轴承故障分类。结果表明:所提出的方法在凯斯西储大学轴承数据集以及德国Paderborn数据集中的精确率分别为98.22%、97.94%,在轴承故障诊断中,所提算法不仅在理论上融合了自动化控制理论与控制工程原理,而且在实践中验证了其在轴承故障诊断中的有效性,为实现轴承故障的早期预警和智能诊断提供了新的技术途径。

In order to address the issue of insufficient feature extraction and inadequate representation of bearing fault characteristics under variable load conditions,a bearing fault diagnosis algorithm based on bilinear pooling guided feature fusion was proposed.Firstly,preprocessing was applied to the acquired raw signal data,involving operations such as DC component removal,noise filtering,antialiasing filtering,and time-domain windowing,to enhance the quality of the vibration spectrogram after signal processing.Secondly,Fourier transformation was performed on the preprocessed signal data to compute amplitude and frequency data after transformation,fol⁃lowed by the creation of corresponding vibration spectrograms.Subsequently,the Res2Net network was enhanced with channel attention and spatial attention mechanisms to extract visual features from different points of interest,and these features were fused using a bilinear pooling method.Finally,a classification head was constructed using fully connected layers and a softmax function to achieve bearing fault classification.The results show that the accuracy of the proposed method in the bearing data set of Case Western Reserve University and the Paderborn data set of Germany is 98.22%and 97.94%,respectively.In the bearing fault diagnosis,the proposed method not only integrates the theory of automation control and the principle of control engineering in theory but also verifies its effectiveness in bearing fault diagnosis in practice,which provides a new technical approach for early warning and intelligent diagnosis of bearing faults.