风力涡轮机变桨轴承的剥落尺寸估算:观测、信号处理方法和实验

Chao Zhang, Long Zhang
{"title":"风力涡轮机变桨轴承的剥落尺寸估算:观测、信号处理方法和实验","authors":"Chao Zhang, Long Zhang","doi":"10.1177/14759217241243108","DOIUrl":null,"url":null,"abstract":"It is essential to continuously monitor the spall size of wind turbine pitch bearings to prevent severe faults and catastrophic failure. In the field of spall size estimation for bearings, an essential step is to extract the entry and impact signals simultaneously. And this would become more difficult when it comes to the wind turbine pitch bearings due to the limited fault signals and heavy noise. In this paper, a coherent procedure is proposed to estimate the spall size for wind turbine pitch bearings. Firstly, the characteristics of entry and impact signals in actual wind turbine pitch bearings are observed to be low-frequency and high-frequency dominated, respectively. On the basis of characteristics analysis, a novel two-stage signal processing method called the wavelet augmented sparse dictionary, is proposed to extract the entry and impact signals, which combines the discrete wavelet transform and sparse representation technique. Finally, the spall size is calculated according to aforementioned extraction and geometric constraints. Results from real-world experiments demonstrate the effectiveness of the proposed method.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"2 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spall size estimation for wind turbine pitch bearings: observation, signal processing method and experiments\",\"authors\":\"Chao Zhang, Long Zhang\",\"doi\":\"10.1177/14759217241243108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is essential to continuously monitor the spall size of wind turbine pitch bearings to prevent severe faults and catastrophic failure. In the field of spall size estimation for bearings, an essential step is to extract the entry and impact signals simultaneously. And this would become more difficult when it comes to the wind turbine pitch bearings due to the limited fault signals and heavy noise. In this paper, a coherent procedure is proposed to estimate the spall size for wind turbine pitch bearings. Firstly, the characteristics of entry and impact signals in actual wind turbine pitch bearings are observed to be low-frequency and high-frequency dominated, respectively. On the basis of characteristics analysis, a novel two-stage signal processing method called the wavelet augmented sparse dictionary, is proposed to extract the entry and impact signals, which combines the discrete wavelet transform and sparse representation technique. Finally, the spall size is calculated according to aforementioned extraction and geometric constraints. Results from real-world experiments demonstrate the effectiveness of the proposed method.\",\"PeriodicalId\":515545,\"journal\":{\"name\":\"Structural Health Monitoring\",\"volume\":\"2 5\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Structural Health Monitoring\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/14759217241243108\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Health Monitoring","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/14759217241243108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

持续监测风力涡轮机变桨轴承的剥落尺寸对防止严重故障和灾难性失效至关重要。在轴承剥落尺寸估算领域,一个必不可少的步骤是同时提取进入信号和冲击信号。由于风力发电机变桨轴承的故障信号有限且噪声较大,这将变得更加困难。本文提出了一种连贯的程序来估算风力发电机变桨轴承的剥落尺寸。首先,观察了实际风力发电机变桨轴承中进入信号和冲击信号分别以低频和高频为主的特征。在特征分析的基础上,结合离散小波变换和稀疏表示技术,提出了一种新颖的两阶段信号处理方法--小波增强稀疏字典,用于提取进入信号和冲击信号。最后,根据上述提取和几何约束条件计算出缺口大小。实际实验结果证明了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spall size estimation for wind turbine pitch bearings: observation, signal processing method and experiments
It is essential to continuously monitor the spall size of wind turbine pitch bearings to prevent severe faults and catastrophic failure. In the field of spall size estimation for bearings, an essential step is to extract the entry and impact signals simultaneously. And this would become more difficult when it comes to the wind turbine pitch bearings due to the limited fault signals and heavy noise. In this paper, a coherent procedure is proposed to estimate the spall size for wind turbine pitch bearings. Firstly, the characteristics of entry and impact signals in actual wind turbine pitch bearings are observed to be low-frequency and high-frequency dominated, respectively. On the basis of characteristics analysis, a novel two-stage signal processing method called the wavelet augmented sparse dictionary, is proposed to extract the entry and impact signals, which combines the discrete wavelet transform and sparse representation technique. Finally, the spall size is calculated according to aforementioned extraction and geometric constraints. Results from real-world experiments demonstrate the effectiveness of the proposed method.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信