FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2

ZHANG TianYuan; SUN HuEr; ZHU JiYang; ZHAO Yang

doi:10.16579/j.issn.1001.9669.2024.04.004

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FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2

Vibration · Noise · Monitoring · Diagnosis | 更新时间：2024-12-26

- FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2
- Journal of Mechanical Strength Vol. 46, Issue 4, Pages: 787-794(2024)
- 作者机构：
  
  1. 中北大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2024.04.004
  CLC： TH165.3;TH17
- Published：15 August 2024，
- 稿件说明：
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ZHANG TIANYUAN, SUN HUER, ZHU JIYANG, et al. FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2. [J]. Journal of mechanical strength , 2024, 46(4): 787-794.
DOI：

ZHANG TIANYUAN, SUN HUER, ZHU JIYANG, et al. FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2. [J]. Journal of mechanical strength , 2024, 46(4): 787-794. DOI： 10.16579/j.issn.1001.9669.2024.04.004.

摘要

针对传统的滚动轴承故障诊断方法难以准确高效的实现故障分类

提出了一种融合对称点模式(Symmetrized Dot Pattern

SDP)和改进SAM⁃MobileNetv2的滚动轴承故障分类方法。首先

将轴承振动信号通过SDP算法转化为含有丰富特征信息的二维图像。然后

将二维图像输入到改进SAM⁃MobileNetv2网络模型中

对故障特征信息进行提取和分类。在改进SAM⁃MobileNetv2网络中

使用自适应激活函数ACON(Activate or not)对SAM⁃MobileNetv2中的ReLU6激活函数进行替换

提高模型分类性能。最后

将本模型与多种网络模型做对比。试验结果表明

本模型可以准确高效地实现对滚动轴承故障的分类

使用凯斯西储大学轴承故障数据的准确率为99.5%

使用渥太华大学轴承故障数据的准确率为97.2%。

Abstract

Traditional fault diagnosis methods for rolling bearings are difficult to accurately and efficiently achieve fault classification.A method of rolling bearing fault classification based on symmetrized dot pattern(SDP)and improved SAM⁃MobileNetv2 was proposed.Firstly

the bearing vibration signal was transformed into two⁃dimensional images with rich characteristic information by SDP algorithm.Secondly

the two⁃dimensional images were fed into the SAM⁃MobileNetv2 network model

which extracted and classified fault feature information.Improved SAM⁃MobileNetv2 networks used the adaptive activation function ACON to replace the ReLU6 activation function in SAM⁃MobileNetv2 to improve model classification performance.Finally

this model was compared with various models.The experimental results show that this model can accurately and efficiently realize the classification of rolling bearing faults

using Case Western Reserve University bearing fault data with an accuracy rate of 99.5%

using the University of Ottawa bearing failure data with an accuracy rate of 97.2%.

关键词

滚动轴承对称点模式SAM-MobileNetv2模型故障诊断

Keywords

Rolling bearingSymmetrized dot patternSAM-MobileNetv2 modelFault diagnosis

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