期刊论文

中文

机械工程师

1002-2333

2024

03

38-43

本校为第一机构

为解决旋转机械故障类型多、等级不均衡的故障诊断难题，构建了一种基于ID3决策树与卷积神经网络（ID3-CNN）的故障诊断模型。首先对原始信号进行人工时域特征提取，使用t-SNE降维可视化提取出特征混叠的故障，而后利用卷积运算对特征混叠的故障进行二次特征提取，提高模型的特征表达能力，最后使用ID3决策树和卷积神经网络对不同等级的故障进行分类。在轴承数据集上对模型进行了验证，结果表明，严重故障的诊断准确率达到100%，轻微故障的诊断准确率达到95%。与传统的支持向量机及二维卷积神经网络比较，提高了模型的诊断准确率及特征提取能力。

To solve the diagnostic problem of multiple rotating mechanical fault types and unbalanced grades,this paper constructs a fault diagnosis model based on ID3 decision tree and convolutional neural network(ID3-CNN).The original signal artificial time domain feature extraction is carried out,t-SNE dimension reduction visualization is used to extract feature overlapping fault.And then the feature aliasing fault secondary feature extraction is performed using the convolution operation to improve the feature expression ability of the model.Finally th...