国外文献 > 1.轴承 > 8. 系统与应用 > 电机与旋转机械
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| 标 题： |
A deep feature enhanced reinforcement learning method for rolling bearing fault diagnosis
一种深度特征增强强化学习方法用于滚动轴承故障诊断 |
| 作 者： |
Wang, Ruixin
Jiang, Hongkai
Zhu, Ke
Wang, Yanfeng
Liu, Chaoqiang |
| 关键词： |
Rolling bearing [滚动轴承]
Fault diagnosis [ 故障诊断]
Reinforcement learning [ 强化学习]
Deep Q network [ 深度Q网络]
Attention model [ 注意模型]
ROTATING MACHINERY [ 旋转机械]
FEATURE-EXTRACTION [ 特征提取] |
| 出版年： |
2022 |
| 期刊名： |
Advanced engineering informatics
高级工程信息学 |
| 年卷期： |
2022, vol.54 |
| 摘 要： |
Fault diagnosis of rolling bearing is crucial for safety of large rotating machinery. However, in practical engi-neering, the fault modes of rolling bearings are usually compound faults and contain a large amount of noise, which increases the difficulty of fault diagnosis. Therefore, a deep feature enhanced reinforcement learning method is proposed for the fault diagnosis of rolling bearing. Firstly, to improve robustness, the neural network is modified by the Elu activation function. Secondly, attention model is used to improve the feature enhanced ability and acquire essential global information. Finally, deep Q network is established to accurately diagnosis the fault modes. Sufficient experiments are conducted on the rolling bearing dataset. Test result shows that the proposed method is superior to other intelligent diagnosis methods.
滚动轴承的故障诊断对于大型旋转机械的安全至关重要。然而在实际工程中，滚动轴承的故障模式通常为复合故障，并且包含大量噪声，这增加了故障诊断的难度。因此，本文提出了一种基于深度特征增强强化学习的方法用于滚动轴承的故障诊断。首先，通过引入Elu激活函数对神经网络进行改进，以提升鲁棒性； 其次，采用注意力模型以增强特征提取能力，并获取关键的全局信息； 最后，构建深度Q网络，实现对故障模式的准确诊断。在滚动轴承数据集上进行了充分的实验，测试结果表明，所提方法优于其他智能诊断方法。 |
| 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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