• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

研究振动信号长度对基于小波散射变换的轴承故障分类的影响。

Investigating the Effect of Vibration Signal Length on Bearing Fault Classification Using Wavelet Scattering Transform.

作者信息

Janjarasjitt Suparerk

机构信息

Department of Electrical and Electronic Engineering, Ubon Ratchathani University, 85 Sathonlamak, Warin Chamrap, Ubon Ratchathani 34190, Thailand.

出版信息

Sensors (Basel). 2025 Jan 24;25(3):699. doi: 10.3390/s25030699.

DOI:10.3390/s25030699
PMID:39943337
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11819883/
Abstract

Bearing condition monitoring and prognosis are crucial tasks for ensuring the proper operation of rotating machinery and mechanical systems. Vibration signal analysis is one of the most effective techniques for bearing condition monitoring and prognosis. In this study, the wavelet scattering transform, derived from wavelet transforms and incorporating concepts from scattering transform and convolutional network architectures, was utilized to extract quantitative features from vibration signals. The number of wavelet scattering coefficients obtained from vibration signals of different lengths varied due to the use of predefined wavelet and scaling filters in the wavelet scattering network. Additionally, these wavelet scattering coefficients are associated with different scattering paths within the corresponding wavelet scattering networks. Eight different lengths of vibration signals, associated with fifteen classes of rolling element bearing faults and conditions, were investigated using wavelet scattering feature extraction. The classes of rolling element bearing faults and conditions included normal bearings as well as ball and inner race faults with various fault diameters ranging from 0.007 inches to 0.028 inches. For the specific dataset validated, the computational results indicated that excellent bearing fault classification was achievable using wavelet scattering feature vectors derived from vibration signals with lengths of at least 6000 samples.

摘要

轴承状态监测与故障预测是确保旋转机械和机械系统正常运行的关键任务。振动信号分析是轴承状态监测与故障预测最有效的技术之一。在本研究中，从小波变换衍生而来并融合了散射变换和卷积网络架构概念的小波散射变换，被用于从振动信号中提取定量特征。由于在小波散射网络中使用了预定义的小波和尺度滤波器，从不同长度的振动信号中获得的小波散射系数数量有所不同。此外，这些小波散射系数与相应小波散射网络内的不同散射路径相关。使用小波散射特征提取方法，研究了与十五类滚动轴承故障及状态相关的八种不同长度的振动信号。滚动轴承故障及状态类别包括正常轴承以及不同故障直径范围从0.007英寸到0.028英寸的滚珠和内圈故障。对于经过验证的特定数据集，计算结果表明，使用从长度至少为6000个样本的振动信号中提取的小波散射特征向量，可以实现出色的轴承故障分类。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/1627510ef9d0/sensors-25-00699-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/78aa454cfc72/sensors-25-00699-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/cdd706307f53/sensors-25-00699-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/5016bbca4da8/sensors-25-00699-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/d9db9a5949c5/sensors-25-00699-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/e57c3dce94f1/sensors-25-00699-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/1627510ef9d0/sensors-25-00699-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/78aa454cfc72/sensors-25-00699-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/cdd706307f53/sensors-25-00699-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/5016bbca4da8/sensors-25-00699-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/d9db9a5949c5/sensors-25-00699-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/e57c3dce94f1/sensors-25-00699-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a9/11819883/1627510ef9d0/sensors-25-00699-g006.jpg

相似文献

1
Investigating the Effect of Vibration Signal Length on Bearing Fault Classification Using Wavelet Scattering Transform.研究振动信号长度对基于小波散射变换的轴承故障分类的影响。
Sensors (Basel). 2025 Jan 24;25(3):699. doi: 10.3390/s25030699.
2
Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features.基于小波散射变换特征的感应电动机轴承故障分类框架。
Sensors (Basel). 2022 Nov 19;22(22):8958. doi: 10.3390/s22228958.
3
WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing.用于滚动轴承故障诊断的WPD增强深度图对比学习数据融合
Micromachines (Basel). 2023 Jul 21;14(7):1467. doi: 10.3390/mi14071467.
4
A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals.基于新统计特征的振动信号轴承故障诊断方法。
Sensors (Basel). 2022 Mar 4;22(5):2012. doi: 10.3390/s22052012.
5
Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry.用于支持包装材料行业全面生产维护的轴承在线状态监测
Sensors (Basel). 2016 Mar 1;16(3):316. doi: 10.3390/s16030316.
6
A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network.基于卷积神经网络的旋转机械故障诊断新方法。
Sensors (Basel). 2018 May 4;18(5):1429. doi: 10.3390/s18051429.
7
Rolling Bearing Fault Diagnosis Based on a Synchrosqueezing Wavelet Transform and a Transfer Residual Convolutional Neural Network.基于同步挤压小波变换和迁移残差卷积神经网络的滚动轴承故障诊断
Sensors (Basel). 2025 Jan 8;25(2):325. doi: 10.3390/s25020325.
8
A novel feature extraction method for bearing fault classification with one dimensional ternary patterns.一种基于一维三元模式的轴承故障分类特征提取新方法。
ISA Trans. 2020 May;100:346-357. doi: 10.1016/j.isatra.2019.11.006. Epub 2019 Nov 7.
9
Identification of localized defects and fault size estimation of taper roller bearing (NBC_30205) with signal processing using the Shannon entropy method in MATLAB for automobile industries applications.在MATLAB中使用香农熵方法通过信号处理来识别汽车工业应用中圆锥滚子轴承（NBC_30205）的局部缺陷并估计故障尺寸
Heliyon. 2022 Dec 5;8(12):e12053. doi: 10.1016/j.heliyon.2022.e12053. eCollection 2022 Dec.
10
A feature extraction method based on information theory for fault diagnosis of reciprocating machinery.基于信息理论的往复机械故障诊断特征提取方法。
Sensors (Basel). 2009;9(4):2415-36. doi: 10.3390/s90402415. Epub 2009 Apr 1.

本文引用的文献

1
Lite and Efficient Deep Learning Model for Bearing Fault Diagnosis Using the CWRU Dataset.基于 CWRU 数据集的轻量级高效深度学习模型在轴承故障诊断中的应用。
Sensors (Basel). 2023 Mar 15;23(6):3157. doi: 10.3390/s23063157.
2
Evaluation of Different Bearing Fault Classifiers in Utilizing CNN Feature Extraction Ability.利用卷积神经网络特征提取能力评估不同轴承故障分类器。
Sensors (Basel). 2022 Apr 26;22(9):3314. doi: 10.3390/s22093314.
3
Understanding deep convolutional networks.理解深度卷积网络。
Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065):20150203. doi: 10.1098/rsta.2015.0203.
4
Invariant scattering convolution networks.不变散射卷积网络。
IEEE Trans Pattern Anal Mach Intell. 2013 Aug;35(8):1872-86. doi: 10.1109/TPAMI.2012.230.
5
Minimum redundancy feature selection from microarray gene expression data.从微阵列基因表达数据中进行最小冗余特征选择。
J Bioinform Comput Biol. 2005 Apr;3(2):185-205. doi: 10.1142/s0219720005001004.