| 摘要: |
| :目的 为了提高水泥生产效率,实现水泥辊压机轴承实时故障诊断,解决工程现场难以获得大量标签故障数
据用于训练的问题。 方法 提出一种使用连续小波卷积层的 Transformer 模型,通过使用小波核替换传统卷积核,作
为特征提取网络第一层,提高网络对于机械故障特征的学习能力;再通过设计一种掩码训练的自监督学习训练,使
网络从大量无标签数据中学习有用的相关特征,并通过设计一种对比损失迁移学习策略对模型进行训练,从相关
公开数据中学习领域通用的判别特征;最后通过微调,实现水泥辊压机故障诊断网络的训练。 结果 实验验证:本模
型可以有效提高诊断进度,并且采用自监督学习与迁移学习训练策略后,在只有训练集 1%数据的情况下,模型微
调仍可达到 78%的准确率;在只有 10%训练集数据的情况下,就可以接近监督训练使用 100%训练集数据下的性
能。 结论 本方法可以有效解决工程实际中无法采集大量标签故障数据的问题,提高模型诊断精度,在少量的目标
数据下,保证模型的诊断精度。 |
| 关键词: 轴承故障诊断 水泥辊压机 小样本 自监督学习 |
| DOI： |
| 分类号: |
| 基金项目: |
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| A Fault Diagnosis Method for Roller Press Bearings Based on Self-supervised Learning |
|
SHU Qingyu
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|
School of Artificial Intelligence Anhui University of Science and Technology Huainan 232000 Anhui China
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| Abstract: |
| Objective This study aims to improve the efficiency of cement production realize real-time fault diagnosis of
cement roller press bearings and solve the problem that it is difficult to obtain a large amount of labeled fault data for
training in the engineering field. Methods A Transformer model using continuous wavelet convolution layers is proposed.
The model enhances the network?? s learning ability for mechanical fault features by using wavelet kernels instead of
traditional convolutional kernels in the first layer of the feature extraction network. It then utilizes mask-training selfsupervised learning to enable the network to learn useful relevant features from a large amount of unlabeled data.
Additionally the model is trained using a designed contrastive loss transfer learning strategy to learn domain-general
discriminative features from relevant public data. Finally through fine-tuning the model completes training of the cement
roller press fault diagnosis network. Results Experimental validation shows that this model effectively accelerates
diagnostic progress. With the self-supervised learning and transfer learning training strategies the model achieves an
accuracy of 78% in fine-tuning with only 1% of the training set data approaching the performance of supervised training
using 100% of the data with only 10% of the training set data. Conclusion This method effectively addresses the
challenge of acquiring large amounts of labeled fault data in engineering practice improving diagnostic accuracy and
maintaining diagnostic precision with limited target data. |
| Key words: bearing fault diagnosis cement roller press small sample self-supervised learning |
