国外文献 > 1.轴承 > 5. 监测与诊断 > 寿命与维护
关键词搜索：
| 标 题： |
A new convolutional dual-channel Transformer network with time window concatenation for remaining useful life prediction of rolling bearings
一种基于时间窗口拼接的新型卷积双通道Transformer网络，用于滚动轴承剩余使用寿命预测 |
| 作 者： |
Jiang, Li
Zhang, Tianao
Lei, Wei
Zhuang, Kejia
Li, Yibing |
| 关键词： |
Remaining useful life [剩余使用寿命]
Rolling bearings [ 滚动轴承]
Causal convolution [ 因果卷积]
Dual-channel [ 双通道]
Transformer [ 变压器]
FAULT-DIAGNOSIS [ 故障诊断]
ALGORITHM [ 算法]
MACHINE [ 机器]
DRIVEN [ 驱动] |
| 出版年： |
2023 |
| 期刊名： |
Advanced engineering informatics
高级工程信息学 |
| 年卷期： |
2023, vol.56 |
| 摘 要： |
Deep learning has achieved numerous breakthroughs in bearing predicting remaining useful life (RUL). However, the current mainstream deep learning framework inevitably has flaws, including the disadvantage of the small receptive field, the difficulty of learning long-term dependencies and the singularity of feature extraction domains, etc. Given the challenges mentioned above, we propose a new convolutional dual-channel Transformer network (CDCT) for remaining useful life prediction of rolling bearings. In the CDCT, the causal convolution operation is applied to extract local features from the time and frequency domains and add positional encoding to the input signal, while the transformer block is utilized for extracting bidirectional features and fusing them. The CDCT not only has a global receptive field but also can learn long-term dependencies regardless of sequence length. Besides, the time window concatenation is adopted to ameliorate the problem of large amounts of trainable parameters of the Transformer-based models. In the experiments, we conduct a detailed analysis of each crucial element and hyperparameter of the CDCT and compare it to multiple basic and advanced methods. The experimental results highlight the superiority of the CDCT in bearing RUL prediction and demonstrate the effectiveness of crucial elements in the CDCT.
深度学习在滚动轴承剩余使用寿命（RUL）预测方面取得了诸多突破。然而，当前主流的深度学习框架不可避免地存在一些缺陷，例如感受野较小、难以学习长期依赖性以及特征提取域的奇异性等。针对上述挑战，我们提出了一种新的卷积双通道Transformer网络（CDCT），用于滚动轴承的剩余使用寿命预测。在CDCT中，通过因果卷积操作从时域和频域提取局部特征，并在输入信号中添加位置编码； 同时利用Transformer模块提取双向特征并进行融合。CDCT不仅具备全局感受野，还能不受序列长度限制地学习长期依赖关系。此外，还采用了时间窗口拼接方法，以缓解基于Transformer模型训练参数过多的问题。在实验中，我们对CDCT的每个关键要素和超参数进行了详细分析，并与多种基础及先进方法进行了比较。实验结果凸显了CDCT在轴承剩余使用寿命（RUL）预测中的优越性，并证明了CDCT关键要素的有效性。 |
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