摘要：

滚动轴承是机械设备中的常用部件, 传统方法难以对多噪声环境下具有众多复杂特征的信号进行分类, 并且常在一维数据上通过经典深度学习模型进行故障诊断, 无法对复杂特征进行充分提取。因此, 提出一种基于ACNN-LFSwin Transformer的双通道故障诊断方法, 分别在一维数据和二维图像上进行故障诊断。首先, 对原始信号分别进行基于完全自适应指数模型分解(CEEMDAN)与短时傅里叶变换(STFT)处理, 获取模态分量(IMF)与二维图像; 然后, 在通道1中将CEEMDAN分解后的IMF送入基于注意力机制的卷积神经网络(ACNN)中进行特征提取, 在通道2中将轴承数据构成的二维图像作为局部特征提取的Swin Transformer网络(LFSwin Transformer)的输入, 进行图像特征提取; 最后, 将两通道特征进行串联融合, 以进行故障诊断, 其中, ACNN运用注意力机制对信号特征进行自动权重分配, 以强调关键特征, LFSwin Transformer模型在传统Swin Transformer的基础上进行向量转换, 将输入向量转换为图像并对其进行卷积操作, 使模型在故障局部特征提取方面更具优势。分别采用CWRU数据集和帕德博恩数据集进行实验验证, 结果表明, 该方法的故障诊断准确率达97%以上, 说明所提方法不仅能对多种故障进行精确诊断, 还能有效避免复杂噪声的干扰。

关键词: 滚动轴承, 故障诊断, 卷积神经网络, 短时傅里叶变换, Swin Transformer

Abstract:

Rolling bearings are components commonly used in mechanical equipment. Traditional methods struggle to classify signals with numerous complex features in a multi-noise environment. They often rely on classical deep learning models for performing fault diagnosis using one-dimensional data, failing to fully extract complex features. To address this issue, this paper proposes a dual-channel fault diagnosis method based on the ACNN-LFSwin Transformer, which performs fault diagnosis on both one-dimensional data and two-dimensional images. First, the original signal is processed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Short-Time Fourier Transform (STFT) to obtain Intrinsic Mode Functions (IMF) and two-dimensional images. Subsequently, in channel 1, the CEEMDAN-decomposed IMF are fed into an Attention-based Convolutional Neural Network (ACNN) for feature extraction. In channel 2, the two-dimensional images composed of bearing data are input into a Swin Transformer network (LFSwin Transformer) for local feature extraction. Finally, the features from both channels are concatenated and fused for fault diagnosis. ACNN employs an attention mechanism to automatically allocate weights to signal features, thereby emphasizing key features. The LFSwin Transformer performs vector conversion based on the traditional Swin Transformer, converts the input vector into an image, and performs convolution operations, making the model more advantageous in extracting local fault features. In experiments on the CWRU and Paderborn datasets, the proposed method achieves a fault diagnosis accuracy of over 97%. This result shows that it can accurately diagnose various faults and effectively avoid interference from complex noise.

Key words: rolling bearing, fault diagnosis, Convolutional Neural Network (CNN), Short-Time Fourier Transform (STFT), Swin Transformer