基于不平衡样本数据的轴承故障诊断模型与方法研究

张伟业; 缪维跑; 苏欢欢; 李春

doi:10.19805/j.cnki.jcspe.2025.240262

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基于不平衡样本数据的轴承故障诊断模型与方法研究

数字化与智能化 | 更新时间：2026-07-27

- 基于不平衡样本数据的轴承故障诊断模型与方法研究
- Research on Bearing Fault Diagnosis Model and Method Based on Unbalanced Sample Data
- 动力工程学报 2025年45卷第6期页码：904-912
- 作者机构：
  
  上海理工大学能源与动力工程学院,上海,200093
- 作者简介：
  
  张伟业(1998—),男,江苏南通人,硕士研究生,研究方向为深度学习与旋转机械系统故障诊断
- 基金信息：
  
  国家自然科学基金资助项目(52375193,52376204,52106262,52006148,51976131);上海市IV类高峰学科-能源科学与技术-上海非碳基能源转换与利用研究院建设资助项目
- DOI：10.19805/j.cnki.jcspe.2025.240262
  中图分类号：
- 网络首发：2025-06-16，
  
  纸质出版：2025
- 稿件说明：
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张伟业,缪维跑,苏欢欢,李春. 基于不平衡样本数据的轴承故障诊断模型与方法研究动力工程学报, 2025, 45(6): 904-912 https://doi.

org/10.19805/j.cnki.jcspe.2025.240262
张伟业,缪维跑,苏欢欢,李春. 基于不平衡样本数据的轴承故障诊断模型与方法研究动力工程学报, 2025, 45(6): 904-912 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240262.

org/10.19805/j.cnki.jcspe.2025.240262 DOI：

摘要

为解决滚动轴承不平衡样本的诊断问题

提出了将条件生成对抗网络与多尺度注意力机制卷积神经网络相融合的诊断方法

通过该方法填充不平衡样本

采用分形盒维数筛选最佳生成样本

输入多尺度注意力机制卷积神经网络完成故障特征提取

并与原不平衡数据和传统样本填充方法进行对比。结果表明:所提方法有较高的识别准确率

可扩充小样本故障数据

有效解决不平衡数据下轴承故障分类问题。

Abstract

In order to solve the problems of rolling bearing unbalance sample diagnosis

a diagnostic method integrating conditional generative adversarial networks with multi-scale attention mechanism convolutional neural networks was proposed. By using this method to fill the unbalanced samples

the best generated samples were screened through fractal box dimension. The multi-scale attention mechanism convolutional neural network was input to complete the fault feature extraction. Then it was compared with the original unbalanced data and the traditional sample filling methods. Results show that the method has a high recognition accuracy

can expand the small sample fault data

and solve the problem of bearing fault classification under the unbalanced data effectively.

关键词

Keywords

references

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