振动映射图像驱动的机械传动系统多部件智能故障诊断方法

樊红卫1, 2, 张腾1, 胥皓文1, 李青山1, 陈步冉1, 李鹏飞1

振动与冲击 ›› 2025, Vol. 44 ›› Issue (12) : 201-211.

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振动与冲击 ›› 2025, Vol. 44 ›› Issue (12) : 201-211.
故障诊断分析

振动映射图像驱动的机械传动系统多部件智能故障诊断方法

  • 樊红卫*1,2,张腾1,胥皓文1,李青山1,陈步冉1,李鹏飞1
作者信息 +

Intelligent multi-component fault diagnosis method driven by vibration-mapped images of mechanical transmission system

  • FAN Hongwei*1,2,ZHANG Teng1,XU Haowen1,LI Qingshan1,CHEN Buran1,LI Pengfei1
Author information +
文章历史 +

摘要

为实现低转速、弱故障和少测点下机械传动系统轴承、转子和齿轮多部件智能故障诊断,提出了一种基于改进振动映射灰度纹理图像和多尺度轻量化卷积神经网络的故障诊断方法。通过对传统灰度图像进行鲁棒局部二值化(Robust local binary pattern, RLBP)得到特征增强的灰度纹理图像。基于标准卷积神经网络(Convolutional neural network, CNN),通过添加批归一化、多尺度卷积和简化全连接层,构建了多尺度轻量化卷积神经网络(Multi-scale lightweight CNN, MSLCNN)模型。设计了700r/min下单测点故障诊断实验,模拟了轴承、转子和齿轮的7种典型故障。研究表明,所提故障诊断方法的模型参数量为0.30M,浮点运算次数为127.22M,模型大小为1.17MB,平均诊断准确率为98.42%,为数据驱动基于深度学习的机械传动系统多部件故障诊断提供了新的可行途径。

Abstract

In order to realize the intelligent multi-component fault diagnosis of bearings, rotors and gears in a mechanical transmission system under low speed, weak faults and few measurement points, a fault diagnosis method based on improved vibration-mapped gray texture images (GTIs) and multi-scale lightweight convolutional neural network is proposed. An improved gray texture image with enhanced features is obtained by robust local binary pattern (RLBP) on the traditional gray image. Based on the standard convolutional neural network (CNN), a multi-scale lightweight CNN (MSLCNN) model is constructed by adding batch normalization, multi-scale convolution and simplified full connection layer. The fault diagnosis experiment under 700r/min and a single measurement point is designed, and seven typical faults of bearings, rotors and gears are simulated. The investigation shows that the model parameters of the proposed fault diagnosis method are 0.30M, the floating point operations (FLOPs) are 127.22M, the model size is 1.17MB, and the average diagnostic accuracy is 98.42%. It provides a new feasible path for the data-driven multi-component fault diagnosis of the mechanical transmission system based on the deep learning.

关键词

传动系统 / 故障诊断 / 灰度纹理图像 / 卷积神经网络

Key words

Transmission system / Fault diagnosis / Gray texture image / Convolutional neural network 

引用本文

导出引用
樊红卫1, 2, 张腾1, 胥皓文1, 李青山1, 陈步冉1, 李鹏飞1. 振动映射图像驱动的机械传动系统多部件智能故障诊断方法[J]. 振动与冲击, 2025, 44(12): 201-211
FAN Hongwei1, 2, ZHANG Teng1, XU Haowen1, LI Qingshan1, CHEN Buran1, LI Pengfei1. Intelligent multi-component fault diagnosis method driven by vibration-mapped images of mechanical transmission system[J]. Journal of Vibration and Shock, 2025, 44(12): 201-211

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