基于IEC模块和双流卷积神经网络的钻孔立杆机钻机轴承故障诊断方法

杨淼; 殷鹏; 曾小军; 陈明; 杨文; 贺继林

doi:10.3969/j.issn.1008-0198.2025.05.018

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基于IEC模块和双流卷积神经网络的钻孔立杆机钻机轴承故障诊断方法

电力工程建设与应用 | 更新时间：2026-01-10

- 基于IEC模块和双流卷积神经网络的钻孔立杆机钻机轴承故障诊断方法
- Research on Bearing Fault Diagnosis Method for Drilling Pole Machine Based on IEC Module, TSCNN
- 湖南电力 2025年45卷第5期页码：133-140
- 作者机构：
  
  1. 国网湖南省电力有限公司电力科学研究院,湖南,长沙,410208
  2. 国网湖南省电力有限公司衡阳供电分公司,湖南,衡阳,421001
  3. 湖南省湘电试验研究院有限公司,湖南,长沙,410208
  4. 国网湖南省电力有限公司邵阳分供电公司,湖南,邵阳,422000
  5. 中南大学机电工程学院,湖南,长沙,410012
- 作者简介：
- 基金信息：
  
  国网湖南省电力有限公司科技项目(5216A5220024);中南大学校企联合项目（10100-718010265）
- DOI：10.3969/j.issn.1008-0198.2025.05.018
  中图分类号： TM754
- 网络出版：2025-11-11，
  
  纸质出版：2025-11-11
- 稿件说明：
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杨淼, 殷鹏, 曾小军, 陈明, 杨文, 贺继林. 基于IEC模块和双流卷积神经网络的钻孔立杆机钻机轴承故障诊断方法[J]. 湖南电力, 2025, 45(5): 133-140.

杨淼, 殷鹏, 曾小军, et al. Research on Bearing Fault Diagnosis Method for Drilling Pole Machine Based on IEC Module, TSCNN[J]. 2025, 45(5): 133-140.
杨淼, 殷鹏, 曾小军, 陈明, 杨文, 贺继林. 基于IEC模块和双流卷积神经网络的钻孔立杆机钻机轴承故障诊断方法[J]. 湖南电力, 2025, 45(5): 133-140. DOI： 10.3969/j.issn.1008-0198.2025.05.018.

杨淼, 殷鹏, 曾小军, et al. Research on Bearing Fault Diagnosis Method for Drilling Pole Machine Based on IEC Module, TSCNN[J]. 2025, 45(5): 133-140. DOI： 10.3969/j.issn.1008-0198.2025.05.018.

摘要

为实现有效的轴承故障诊断

提出一种基于集成InceptionV2、高效通道注意力（efficient channel attention

ECA）、卷积块注意力模块（convolutional block attention module

CBAM)和双流卷积神经网络(two-stream convolutional neural network

TSCNN)的轴承故障诊断方法。首先

利用快速傅里叶变换(fast fourier transform

FFT)和连续小波变换(continuous wavelet transform

CWT)将原始振动信号转换成一维数据和二维时频图像。随后

构建TSCNN融合模型

将得到的小波时频图像和FFT谱作为输入

利用InceptionV2和ECANet-CBAM改进模块提取时频图像的空间特征

将得到的双层特征信息融合到Softmax层中完成故障分类。最后

基于滚动轴承故障标准数据集进行对比分析

结果表明

所提出故障诊断方法诊断准确率更高。

Abstract

To achieve efficient bearing fault diagnosis

a novel method based on the IEC module (integrating InceptionV2

Efficient Channel Attention

and Convolutional Block Attention Module) and a Two-stream Convolutional Neural Network(TSCNN) is proposed. First

raw vibration signals are converted into one-dimensional data and two-dimensional time-frequency images using Fast Fourier Transform(FFT) and Continuous Wavelet Transform(CWT). Subsequently

an improved TSCNN fusion model is constructed

and the obtained wavelet time-frequency images and FFT spectra are used as inputs to extract the spatial features of the time-frequency images by using InceptionV2 and ECANet-CBAM improvement module

and the resulting dual-layer feature information is fused into the Softmax layer to accomplish fault classification. Finally

comparative analysis based on a standard rolling bearing fault data set demonstrates that the proposed IEC-TSCNN method achieves superior diagnostic accuracy.

关键词

Keywords

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