首页|BP神经网络在滚动轴承故障诊断中的应用研究

BP神经网络在滚动轴承故障诊断中的应用研究

扫码查看
反向传播神经网络(Back Propagation Neural Network,BPNN)是一种深度学习模型,在各个领域都有重要应用.文章以滚动轴承故障诊断为例,探讨了BP神经网络在其中的应用.文章通过运用及优化BP神经网络,对凯斯西储大学提供的轴承故障数据加窗后进行离散傅里叶变换处理,再进行峰值特征提取,然后利用该数据进行神经网络模型的学习和预测,构建了一个能够准确预测轴承故障类型的网络模型.该模型能够提高轴承故障诊断的效率和准确性,具有重要的实用价值.
Research on the application of BP neural wetwork in fault diagnosis of rolling bearings
The back propagation neural network(BPNN)is an important deep learning model,which has important applications and advantages in various fields.This article takes the bearing fault diagnosis as an example to mainly discuss the application of BP neural network.In this article,by using and optimizing BP neural network,the bearing fault data provided by Case Western Reserve University is processed by windowing and discrete Fourier transform,and then peak feature extraction is carried out.Then,the neural network model is learned and predicted using this data,and a network model that can accurately predict the bearing fault type is constructed.This model can improve the efficiency and accuracy of bearing fault diagnosis,and has important practical value.

BP neural networkfault diagnosisrolling bearing

王尉旭、周豪、洪朝银

展开 >

重庆交通大学,重庆 400074

BP神经网络 故障诊断 滚动轴承

2024

无线互联科技
江苏省科学技术情报研究所

无线互联科技

影响因子:0.263
ISSN:1672-6944
年,卷(期):2024.21(5)
  • 3