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 L1 Regularization Transfer Learning and LSTM Deep Learning
基于L1正则化迁移学习与LSTM深度学习的轴承故障诊断方法 |
| 作 者： |
Dajie Zhu
Xudong Song
Jie Yang
Yuyang Cong
Lijuan Wang |
| 关键词： |
Fault diagnosis [故障诊断]
L1 regularization [ L1正则化]
Transfer learning [ 迁移学习]
LSTM [ LSTM] |
| 出版年： |
2021 |
| 会议名： |
2021 IEEE International Conference on Information Communication and Software Engineering (ICICSE 2021)
2021年IEEE国际信息通信与软件工程会议（ICICSE 2021） |
| 届 次： |
|
| 摘 要： |
In the practical application of rail transit, it is difficult to obtain bearing fault data and the training data of fault diagnosis model is insufficient, which leads to the low accuracy and generalization ability of fault diagnosis model. In this paper, a new bearing fault diagnosis method based on transfer learning is proposed. Based on transfer learning, we introduce L1 regularization, then adds it to the Long Short-Term Memory (LSTM) classification model, and uses a small amount of target domain data to fine tune the parameters of the model, and finally constructs a bearing fault diagnosis model. In this paper, the bearing data set of Case Western Reserve University is used to test the bearing fault diagnosis model. Compared with the conventional LSTM, Gated Recurrent Unit (GRU) and Bi-LSTM, the model proposed in this paper has higher accuracy in fault diagnosis as well as certain reliability and generalization ability.
在轨道交通的实际应用中，轴承故障数据难以获取，且故障诊断模型的训练数据不足，导致故障诊断模型的准确性和泛化能力较低。本文提出一种基于迁移学习的新型轴承故障诊断方法。通过引入L1正则化，并将其加入长短期记忆网络（LSTM）分类模型中，利用少量目标域数据对模型参数进行微调，最终构建出轴承故障诊断模型。本文采用西大大学的轴承数据集对所提出的模型进行测试。与传统的LSTM、门控循环单元（GRU）和双LSTM相比，该模型在故障诊断方面具有更高的准确率，同时具备一定的可靠性和泛化能力。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
京公网安备 11010202009788