Remaining Useful Life Prediction for Equipment Using Residual Network and Convolutional Attention Mechanism

MO Renpeng; LI Tianmei; SI Xiaosheng; ZHU Xu

doi:10.7652/xjtuxb202204021

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Remaining Useful Life Prediction for Equipment Using Residual Network and Convolutional Attention Mechanism

更新时间：2025-07-09

- Remaining Useful Life Prediction for Equipment Using Residual Network and Convolutional Attention Mechanism
- Vol. 56, Issue 4, Pages: 194-202(2022)
- 作者机构：
  
  火箭军工程大学导弹工程学院,西安,710025
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202204021
  CLC： TH17
- Online First：10 April 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
MO Renpeng, LI Tianmei, SI Xiaosheng, et al. Remaining Useful Life Prediction for Equipment Using Residual Network and Convolutional Attention Mechanism[J]. 2022, 56(4): 194-202.
DOI：

MO Renpeng, LI Tianmei, SI Xiaosheng, et al. Remaining Useful Life Prediction for Equipment Using Residual Network and Convolutional Attention Mechanism[J]. 2022, 56(4): 194-202. DOI： 10.7652/xjtuxb202204021.

摘要

针对以往剩余使用寿命(RUL)预测方法均等对待深层特征中具有不同重要性的空间域和通道域特征

导致大量的计算资源浪费在不重要的特征上

进而造成RUL预测值偏差过大、不能对失效设备进行及时的维护以避免潜在的安全隐患的问题

提出一种采用残差网络与卷积注意力机制的端到端的RUL预测方法。该方法以卷积层和池化层对原始监测信号进行浅层特征提取与压缩; 利用堆叠残差模块在学习深层特征的同时

缓解梯度弥散以及网络退化现象的发生; 由卷积注意力模块对设备的深层退化特征进行加权赋值

分别在其空间维度上和通道维度上强化更重要的特征并抑制相对不重要的特征

使网络的注意力集中在对RUL预测任务更关键的信息上; 将加权后的特征输入到全连接网络中映射得到RUL预测值。通过PHM2012轴承数据集进行了实验验证

实验结果表明

卷积注意力和残差结构皆对改善模型的预测性能有着积极的作用

所提方法在测试轴承上的均方根误差和平均绝对误差分别为0.107 9和0.083 1

远低于其他对比方法。

Abstract

Regarding the previous remaining life prediction methods

the spatial domain and channel domain features of different importance in the deep features are often treated equally

causing a lot of computing resources to be wasted on unimportant features

which leads to excessive deviations in RUL predictions and failure to perform timely maintenance on failed equipment to avoid potential safety hazards

an end-to-end RUL prediction method using residual network and convolutional attention mechanism is proposed. This method first uses the convolutional layer and the pooling layer to extract and compress the shallow features of the original monitoring signal. Then uses the stacked residual module to learn deep features while mitigating the occurrence of gradient dispersion and network degradation Next

the convolutional attention module weights the deep degradation features of the device to strengthen the more important features and suppress the relatively unimportant features in its spatial dimension and channel dimension

so that the network's attention is focused on information that is more critical to RUL prediction task. Finally

the weighted features are input into the fully connected network and mapped to obtain the RUL prediction value. The experimental verification is carried out through the PHM2012 bearing data set. The experimental results show that both the convolutional attention and the residual structure have a positive effect on improving the prediction performance of the model. In addition

the root mean square error and average absolute error of the proposed method on the test bearing are 0.107 9 and 0.083 1

which are much lower than other comparison methods.

关键词

Keywords

references

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