国外文献 > 1.轴承 > 5. 监测与诊断 > 故障诊断
关键词搜索：
| 标 题： |
A Bearing Fault Diagnosis Method Based on 2D Images and Transformer
基于2D图像与变压器的轴承故障诊断方法 |
| 作 者： |
Zhenzhen Tian
Xinyu Zhang
Wei Yan
Jihua Wang |
| 关键词： |
Gramian angular field (GAF) [角加速度场（GAF）]
Short-time fourier transform (STFT) [ 短时傅里叶变换（STFT）]
Wavelet transform (WT) [ 小波变换（WT）]
Swin transformer [ 斯温变压器]
Fault diagnosis [ 故障诊断] |
| 出版年： |
2023 |
| 文集名： |
13th International Conference on Information Technology in Medicine and Education (ITME 2023)
第13届医学与教育信息技术国际会议（ITME 2023） |
| 会议名： |
13th International Conference on Information Technology in Medicine and Education (ITME 2023)
第13届医学与教育信息技术国际会议（ITME 2023） |
| 届 次： |
13th |
| 摘 要： |
Bearings are an important part of rotating machinery, so the accurate diagnosis of bearing faults is essential. Therefore, this paper proposes a bearing fault diagnosis method that combines two-dimensional (2D) images and the Swin Transformer model. The Swin Transformer model takes the attention mechanism as its core, which can efficiently capture global information of images. First, we use three different methods to convert bearing vibration signals into 2D images, namely gramian angular field (GAF), short-time Fourier transform (STFT), and wavelet transform (WT). Secondly, the 2D images are input into the Swin transformer model for feature extraction and fault classification through transfer learning. Finally, the bearing dataset of Case Western Reserve University (CWRU) is used for experiments. The results show that the combination of three different types of 2D images with the Swin Transformer model achieved excellent fault diagnosis results, which is superior to many traditional deep learning methods.
轴承是旋转机械的重要组成部分，因此对轴承故障的准确诊断至关重要。为此，本文提出一种结合二维（2D）图像与Swin Transformer模型的轴承故障诊断方法。Swin Transformer模型以注意力机制为核心，能够高效地捕捉图像的全局信息。首先，我们采用三种不同方法将轴承振动信号转换为二维图像，分别为格拉姆角场（GAF）、短时傅里叶变换（STFT）和小波变换（WT）。其次，将生成的二维图像输入Swin Transformer模型，通过迁移学习进行特征提取和故障分类。最后，使用西密歇根大学（CWRU）的轴承数据集进行实验。结果表明，将三种不同类型的二维图像与Swin Transformer模型相结合，实现了优异的故障诊断效果，优于多种传统深度学习方法。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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