基于1D-CNN的齿轮3D振动信号故障诊断方法

Fault diagnosis method for gear 3D vibration signal based on 1D-CNN

摘要

为了解决从齿轮一维振动信号提取故障特征不全面的问题，提出一种基于一维卷积神经网络（one-dimensional convolutional neural network，1D-CNN）的齿轮3D振动信号故障诊断新方法。首先，提取原始三维振动信号各维的时域特征；其次，利用一维卷积神经网络（1D-CNN）模型进行特征选择；最后，将选择后的特征进行重组，重组特征作为1D-CNN故障诊断模型的输入实现故障分类操作。结果表明，利用提出的故障诊断方法，诊断准确率显著提高。模型的结构简单，训练速度快，能够快速实现故障诊断。

Abstract

In order to solve the problem of incomplete fault feature extraction from gear one-dimensional vibration signal，a new fault diagnosis method of gear 3D vibration signal based on one-dimensional convolution neural network was proposed.Firstly，the time domain features of each dimension of the original three-dimensional vibration signal were extracted；Secondly，the one-dimensional convolutional neural network model was used for feature selection；Finally，the selected features were reorganized，and the reorganized features were used as the input of 1D-CNN fault diagnosis model to achieve fault classification operation.The results show that the accuracy of diagnosis is remarkably improved by using the proposed fault diagnosis method.Moreover，the structure of the model is simple，the training speed is fast，and the fault diagnosis can be achieved quickly.

关键词

三维振动信号 / 时域特征 / 一维卷积神经网络 / 故障诊断 / 齿轮

Key words

three-dimensional vibration signal / time domain features / one-dimensional convolutional neural network / fault diagnosis / gear

基金资助

国家自然科学基金(61903262)

辽宁省教育厅项目(JYT2020021)