双Q小波稀疏分解在风电行星齿轮箱故障特征提取中的应用

Q2 Engineering
Jin Xu, Xiucai Ding, Zhu Zhang, Lang Chen
{"title":"双Q小波稀疏分解在风电行星齿轮箱故障特征提取中的应用","authors":"Jin Xu, Xiucai Ding, Zhu Zhang, Lang Chen","doi":"10.24423/ENGTRANS.1326.20211004","DOIUrl":null,"url":null,"abstract":"The wind turbine gearbox is a critical equipment transforming the speed of the rotor hub to the generator, the condition of which is the reflection of operational efficiency and reliability of wind turbines. As the initial stage of the wind turbine gearbox, the fault feature extraction of the planetary gear set is challenging since it is prone to be affected by complicated structure, vibration from other high-speed stages and background noise. In this paper, a double Q factor wavelet-based sparse decomposition is applied to the fault feature extraction of the wind turbine planetary gearbox. Considering the sparsest wavelet coefficients, the vibration signal is iteratively decomposed into high Q and low Q components. The fault feature is generally hidden in the low Q component. With further demodulation, the fault information of planetary gears can be easily detected.","PeriodicalId":38552,"journal":{"name":"Engineering Transactions","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Application of Double Q Wavelet-based Sparse Decomposition to Fault Feature Extraction of Wind Turbine Planetary Gearbox\",\"authors\":\"Jin Xu, Xiucai Ding, Zhu Zhang, Lang Chen\",\"doi\":\"10.24423/ENGTRANS.1326.20211004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The wind turbine gearbox is a critical equipment transforming the speed of the rotor hub to the generator, the condition of which is the reflection of operational efficiency and reliability of wind turbines. As the initial stage of the wind turbine gearbox, the fault feature extraction of the planetary gear set is challenging since it is prone to be affected by complicated structure, vibration from other high-speed stages and background noise. In this paper, a double Q factor wavelet-based sparse decomposition is applied to the fault feature extraction of the wind turbine planetary gearbox. Considering the sparsest wavelet coefficients, the vibration signal is iteratively decomposed into high Q and low Q components. The fault feature is generally hidden in the low Q component. With further demodulation, the fault information of planetary gears can be easily detected.\",\"PeriodicalId\":38552,\"journal\":{\"name\":\"Engineering Transactions\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Transactions\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24423/ENGTRANS.1326.20211004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Transactions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24423/ENGTRANS.1326.20211004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 1

摘要

风力机齿轮箱是将转子轮毂的转速传递给发电机的关键设备,其运行状况是风力机运行效率和可靠性的体现。行星齿轮组作为风电齿轮箱的初始阶段,容易受到复杂的结构、其他高速阶段的振动和背景噪声的影响,其故障特征提取具有一定的挑战性。本文将双Q因子小波稀疏分解方法应用于风电行星齿轮箱故障特征提取。考虑小波系数的稀疏性,将振动信号迭代分解为高Q分量和低Q分量。故障特征一般隐藏在低Q分量中。通过进一步解调,可以很容易地检测出行星齿轮的故障信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Double Q Wavelet-based Sparse Decomposition to Fault Feature Extraction of Wind Turbine Planetary Gearbox
The wind turbine gearbox is a critical equipment transforming the speed of the rotor hub to the generator, the condition of which is the reflection of operational efficiency and reliability of wind turbines. As the initial stage of the wind turbine gearbox, the fault feature extraction of the planetary gear set is challenging since it is prone to be affected by complicated structure, vibration from other high-speed stages and background noise. In this paper, a double Q factor wavelet-based sparse decomposition is applied to the fault feature extraction of the wind turbine planetary gearbox. Considering the sparsest wavelet coefficients, the vibration signal is iteratively decomposed into high Q and low Q components. The fault feature is generally hidden in the low Q component. With further demodulation, the fault information of planetary gears can be easily detected.
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
Engineering Transactions
Engineering Transactions Engineering-Engineering (all)
CiteScore
1.40
自引率
0.00%
发文量
0
期刊介绍: Engineering Transactions (formerly Rozprawy Inżynierskie) is a refereed international journal founded in 1952. The journal promotes research and practice in engineering science and provides a forum for interdisciplinary publications combining mechanics with: Material science, Mechatronics, Biomechanics and Biotechnologies, Environmental science, Photonics, Information technologies, Other engineering applications. The journal publishes original papers covering a broad area of research activities including: experimental and hybrid techniques, analytical and numerical approaches. Review articles and special issues are also welcome. Following long tradition, all articles are peer reviewed and our expert referees ensure that the papers accepted for publication comply with high scientific standards. Engineering Transactions is a quarterly journal intended to be interesting and useful for the researchers and practitioners in academic and industrial communities.
×
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