采用递归图编码技术与残差网络的滚动轴承故障诊断

张龙; 胡燕青; 赵丽娟; 张号; 王晓博; 文培田

doi:10.7652/xjtuxb202302012

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采用递归图编码技术与残差网络的滚动轴承故障诊断

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- 采用递归图编码技术与残差网络的滚动轴承故障诊断
- Fault Diagnosis of Rolling Bearings Using Recurrence Plot Coding Technique and Residual Network
- 西安交通大学学报 2023年57卷第2期页码：110-120
- 作者机构：
  
  华东交通大学轨道交通基础设施性能监测与保障国家重点实验室,南昌,330013
- 作者简介：
  
  : 2022-06-21。作者简介: 张龙(1980—),男,副教授,硕士生导师。基金项目: 国家自然科学基金资助项目(51665013)
- 基金信息：
  
  : 2022-06-21。作者简介: 张龙(1980—),男,副教授,硕士生导师。基金项目: 国家自然科学基金资助项目(51665013);江西省自然科学基金资助项目(20212BAB204007);江西省研究生创新资金资助项目(YC2020-S335;YC2021-S42)。
- DOI：10.7652/xjtuxb202302012
  中图分类号： TH13;TH17
- 网络首发：2023-02-10，
  
  纸质出版：2023
- 稿件说明：
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张龙, 胡燕青, 赵丽娟, 等. 采用递归图编码技术与残差网络的滚动轴承故障诊断[J]. 西安交通大学学报, 2023,57(2):110-120.

ZHANG Long, HU Yanqing, ZHAO Lijuan, et al. Fault Diagnosis of Rolling Bearings Using Recurrence Plot Coding Technique and Residual Network[J]. 2023, 57(2): 110-120.
张龙, 胡燕青, 赵丽娟, 等. 采用递归图编码技术与残差网络的滚动轴承故障诊断[J]. 西安交通大学学报, 2023,57(2):110-120. DOI： 10.7652/xjtuxb202302012.

ZHANG Long, HU Yanqing, ZHAO Lijuan, et al. Fault Diagnosis of Rolling Bearings Using Recurrence Plot Coding Technique and Residual Network[J]. 2023, 57(2): 110-120. DOI： 10.7652/xjtuxb202302012.

摘要

针对传统故障诊断方法未充分挖掘故障信号的时间序列间关联性特征的问题

将递归图编码技术引入故障诊断领域

提出了递归图编码技术与残差网络的滚动轴承故障诊断模型。采用递归图编码方式将振动信号转换为增强信号特征的二维纹理图像; 将这些特征图像输入残差网络中

结合残差网络对二维图像数据优秀的自适应特征提取能力

对滚动轴承进行故障诊断。使用凯斯西储大学轴承数据集和某局机务段采集的真实机车轴承数据进行试验验证

结果表明:所提模型对轴承故障诊断的识别准确率为99.99%和99.83%; 在输入不同的数据长度和变工况的试验中

所提模型均保持了良好的故障诊断效果; 对比其他常见的故障诊断方法

所提模型拥有更好的泛化性能和识别准确率。

Abstract

Traditional fault diagnosis methods do not fully exploit the correlation characteristics between the time series of fault signals. To address this problem

recurrence plot coding technique is introduced into fault diagnosis

and a rolling bearing fault diagnosis model using this technique and residual network is proposed. Recurrence plots coding converts vibration signals into 2D texture images with enhanced signal features. With these feature images imported

the residual network will exert its excellent capacity to extract adaptive features of 2D image data and carry out fault diagnosis on rolling bearings. Tests are conducted using the Case Western Reserve University bearing dataset and real locomotive bearing data collected from a bureau locomotive depot for validation. In tests

the proposed model yields accuracy of 99.99% and 99.83% in bearing fault detection and delivers good fault diagnosis results with different data lengths and variable operating conditions input. To sum up

the proposed model has better generalization performance and detection accuracy than other common fault diagnosis methods.

关键词

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