• 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

基于RIME-VMD和改进型WKN的初期转子不平衡故障检测

Detection of incipient rotor unbalance fault based on the RIME-VMD and modified-WKN.

作者信息

Wang Qian, Hu Shuo, Wang Xinya

机构信息

College of Electrical Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450000, China.

IoT Equipment Research Institute, GL TECH Co., Ltd., Zhengzhou, 450000, China.

出版信息

Sci Rep. 2024 Feb 26;14(1):4683. doi: 10.1038/s41598-024-54984-z.

DOI:10.1038/s41598-024-54984-z
PMID:38409246
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10897425/
Abstract

Due to the high incidence and inconspicuous initial characteristics of rotor unbalance faults, the detection of incipient unbalance faults is becoming a very challenging problem. In this paper, a new method of small rotor unbalance fault diagnosis based on RIME-VMD and modified wavelet kernel network (modified-WKN) is proposed. Firstly, in order to extract the small unbalance fault information from the vibration signals with low signal-to-noise ratio (SNR) more efficiently, the RIME algorithm is used to search for the optimal location of the penalty factor and decomposition layer in the variable mode decomposition (VMD). Secondly, the most relevant decomposition components to the small unbalance fault information are selected by using Pearson Correlation Coefficients and utilized to reconstruct the signal. Finally, the modified-WKN diagnostic model that is used for multi-sensor data fusion is constructed. The model can acquire features of vibration signals from multiple position sensors, which enhances the ability of the modified WKN diagnostic model to deal with incipient fault modes. Based on the experimental analysis of rotor unbalance fault datasets with different SNRs, it is verified that the detection performance of the proposed method is better than the traditional WKN and VMD-WKN methods. Specifically, the proposed method is more sensitive to the initial unbalance faults.

摘要

由于转子不平衡故障的高发生率和不明显的初始特征，早期不平衡故障的检测正成为一个极具挑战性的问题。本文提出了一种基于RIME-VMD和改进小波核网络（modified-WKN）的小型转子不平衡故障诊断新方法。首先，为了更有效地从低信噪比（SNR）的振动信号中提取微小不平衡故障信息，采用RIME算法在变分模态分解（VMD）中搜索惩罚因子和分解层数的最优位置。其次，利用皮尔逊相关系数选择与微小不平衡故障信息最相关的分解分量，并用于信号重构。最后，构建用于多传感器数据融合的改进小波核网络诊断模型。该模型可以获取来自多个位置传感器的振动信号特征，增强了改进小波核网络诊断模型处理早期故障模式的能力。通过对不同信噪比的转子不平衡故障数据集进行实验分析，验证了所提方法的检测性能优于传统的小波核网络和VMD-WKN方法。具体而言，所提方法对初始不平衡故障更敏感。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/eb7dd9292f18/41598_2024_54984_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/e1ba7c171f1b/41598_2024_54984_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/43b536022a7f/41598_2024_54984_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/3263a55142e9/41598_2024_54984_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/c83165647808/41598_2024_54984_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/d50acf8ac532/41598_2024_54984_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/693410545615/41598_2024_54984_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/6d0761c677a4/41598_2024_54984_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/ce71a8dbb9a4/41598_2024_54984_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/e48c461b57bd/41598_2024_54984_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/72c5001c69f3/41598_2024_54984_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/4c49d5ba90da/41598_2024_54984_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/c74251898ce6/41598_2024_54984_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/73b844537655/41598_2024_54984_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/1ee9b06572de/41598_2024_54984_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/eb7dd9292f18/41598_2024_54984_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/e1ba7c171f1b/41598_2024_54984_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/43b536022a7f/41598_2024_54984_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/3263a55142e9/41598_2024_54984_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/c83165647808/41598_2024_54984_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/d50acf8ac532/41598_2024_54984_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/693410545615/41598_2024_54984_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/6d0761c677a4/41598_2024_54984_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/ce71a8dbb9a4/41598_2024_54984_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/e48c461b57bd/41598_2024_54984_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/72c5001c69f3/41598_2024_54984_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/4c49d5ba90da/41598_2024_54984_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/c74251898ce6/41598_2024_54984_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/73b844537655/41598_2024_54984_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/1ee9b06572de/41598_2024_54984_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1174/10897425/eb7dd9292f18/41598_2024_54984_Fig14_HTML.jpg

相似文献

1
Detection of incipient rotor unbalance fault based on the RIME-VMD and modified-WKN.基于RIME-VMD和改进型WKN的初期转子不平衡故障检测
Sci Rep. 2024 Feb 26;14(1):4683. doi: 10.1038/s41598-024-54984-z.
2
A Novel Method for Identifying Crack and Shaft Misalignment Faults in Rotor Systems under Noisy Environments Based on CNN.基于卷积神经网络的噪声环境下转子系统裂纹和轴不对中故障的新方法。
Sensors (Basel). 2019 Nov 25;19(23):5158. doi: 10.3390/s19235158.
3
A Rolling Bearing Fault Diagnosis Method Based on the WOA-VMD and the GAT.一种基于鲸鱼优化算法-变分模态分解和门控自注意力 Transformer 的滚动轴承故障诊断方法
Entropy (Basel). 2023 Jun 1;25(6):889. doi: 10.3390/e25060889.
4
Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition.基于改进变分模态分解的齿轮箱故障诊断研究。
Sensors (Basel). 2018 Oct 18;18(10):3510. doi: 10.3390/s18103510.
5
Incipient fault diagnosis of bearings based on parameter-optimized VMD and envelope spectrum weighted kurtosis index with a new sensitivity assessment threshold.基于参数优化变分模态分解和具有新灵敏度评估阈值的包络谱加权峭度指数的轴承早期故障诊断
ISA Trans. 2021 Aug;114:413-433. doi: 10.1016/j.isatra.2020.12.041. Epub 2020 Dec 28.
6
Incipient fault diagnosis for the cam-driven absolute gravimeter.凸轮驱动绝对重力仪的早期故障诊断
Rev Sci Instrum. 2022 May 1;93(5):054501. doi: 10.1063/5.0079424.
7
An Improved Bearing Fault Diagnosis Model of Variational Mode Decomposition Based on Linked Extension Neural Network.基于链接扩展神经网络的变分模态分解轴承故障诊断改进模型。
Comput Intell Neurosci. 2022 Apr 25;2022:1615676. doi: 10.1155/2022/1615676. eCollection 2022.
8
A New Compound Fault Feature Extraction Method Based on Multipoint Kurtosis and Variational Mode Decomposition.一种基于多点峭度和变分模态分解的复合故障特征提取新方法。
Entropy (Basel). 2018 Jul 10;20(7):521. doi: 10.3390/e20070521.
9
Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM.基于鲸鱼优化算法-变分模态分解-多尺度排列熵和多粒子群优化-最小二乘支持向量机的滚动轴承故障诊断
Entropy (Basel). 2022 Jul 3;24(7):927. doi: 10.3390/e24070927.
10
Research on fault diagnosis of rolling bearing based on multi-sensor bi-layer information fusion under small samples.小样本下基于多传感器双层信息融合的滚动轴承故障诊断研究
Rev Sci Instrum. 2023 Nov 1;94(11). doi: 10.1063/5.0174359.

引用本文的文献

1
Multi-fault diagnosis and damage assessment of rolling bearings based on IDBO-VMD and CNN-BiLSTM.基于改进的果蝇优化算法-变分模态分解（IDBO-VMD）和卷积神经网络-双向长短期记忆网络（CNN-BiLSTM）的滚动轴承多故障诊断与损伤评估
Sci Rep. 2025 Aug 24;15(1):31121. doi: 10.1038/s41598-025-17177-w.
2
Short-term natural gas load forecasting based on EL-VMD-Transformer-ResLSTM.基于EL-VMD-Transformer-ResLSTM的短期天然气负荷预测
Sci Rep. 2024 Sep 2;14(1):20343. doi: 10.1038/s41598-024-70384-9.

本文引用的文献

1
A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft.一种用于旋转轴不平衡检测的新型深度学习框架。
Sensors (Basel). 2023 Aug 12;23(16):7141. doi: 10.3390/s23167141.
2
A graph neural network-based bearing fault detection method.基于图神经网络的轴承故障检测方法。
Sci Rep. 2023 Mar 31;13(1):5286. doi: 10.1038/s41598-023-32369-y.
3
Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG.基于多传感器信息融合与VGG的齿轮故障诊断方法
Entropy (Basel). 2022 Nov 6;24(11):1618. doi: 10.3390/e24111618.
4
Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment.基于多传感器振动信号的旋转机械设备三阶段故障预测
Entropy (Basel). 2022 Jan 21;24(2):164. doi: 10.3390/e24020164.
5
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study.深度学习算法在旋转机械智能诊断中的应用：一个开源基准研究。
ISA Trans. 2020 Dec;107:224-255. doi: 10.1016/j.isatra.2020.08.010. Epub 2020 Aug 19.