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基于时间卷积网络的机床齿轮箱轴承剩余寿命预测

Remaining Useful Life Prediction of Machine Tool Gearbox Bearings Based on Temporal Convolutional Networks

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  • 维普
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基于深度神经网络的RUL预测模型结构比较复杂,不能很好地满足中长期预测任务的要求.为了更好地利用时间信息,设计一种基于时间卷积网络(TCN)的轴承RUL预测模型.以振动信号的频谱特征作为输入,利用因果膨胀卷积结构提取频域特征并捕获长期依赖,从而实现对轴承准确的RUL预测.为了进一步说明所提方法的优越性,将所提方法与卷积神经网络(CNN)、门控循环单元(GRU)进行了对比.结果表明:所提出的TCN模型的RUL预测精度优于其他现有方法,具有较高的精度.
The currently available deep neural network-based remaining useful life(RUL)prediction models are complex in struc-ture and do not meet the requirements of medium and long-term prediction tasks well.In order to make better use of temporal informa-tion,a temporal convolutional network(TCN)based RUL prediction model for bearings was designed.Taking the spectral features of the vibration signal as input,a causally inflated convolutional structure was used to extract the frequency domain features and capture the long-term dependence to achieve accurate prediction of the bearing RUL.To further illustrate the superiority of the proposed method,comparative experiments were conducted using a convolutional neural network(CNN)and a gated recurrent unit(GRU).The results show that the RUL prediction accuracy of the proposed TCN model outperforms other existing methods with high accuracy.

gearbox bearings of machine toolstemporal convolutional network(TCN)time seriesremaining useful life(RUL)prediction

姜广君、段政伟、穆东明、杨金森

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内蒙古工业大学机械工程学院,内蒙古呼和浩特 010051

内蒙古自治区先进制造技术重点实验室,内蒙古呼和浩特 010051

机床齿轮箱轴承 时间卷积网络 时间序列 剩余寿命预测

内蒙古自治区关键技术攻关计划内蒙古自然科学基金面上项目自治区直属高校基本科研业务费项目自治区直属高校基本科研业务费项目

2021GG03462023MS05030JY20220004JY20230094

2024

10.3969/j.issn.1001-3881.2024.12.032

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子：0.32
ISSN：1001-3881
年,卷(期)：2024.52(12)
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