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Article(id=1222940988096631551, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1222940980886626474, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222298, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1667318400000, receivedDateStr=2022-11-02, revisedDate=1677168000000, revisedDateStr=2023-02-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1769501452706, onlineDateStr=2026-01-27, pubDate=1700841600000, pubDateStr=2023-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769501452706, onlineIssueDateStr=2026-01-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769501452706, creator=13041195026, updateTime=1769501452706, updator=13041195026, issue=Issue{id=1222940980886626474, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='11', pageStart='1', pageEnd='226', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769501450987, creator=13041195026, updateTime=1769501450987, updator=13041195026, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=178, endPage=182, ext={EN=ArticleExt(id=1222940988893549374, articleId=1222940988096631551, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Vibration Feature Extraction for Hydropower Units Based on RFOA Optimized Ensemble Empirical Mode Decomposition Threshold and Sample Entropy, columnId=1222925284869922957, journalTitle=Water Resources and Power, columnName=ELECTROMECHANICS AND CONTROL ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

Aiming at the shortcomings of hydropower unit vibration signal denoising using ensemble empirical mode decomposition (EEMD), a denoising algorithm based on an improved fruit fly optimization algorithm (RFOA) for optimizing the EEMD noise IMF component threshold was proposed. Firstly, the noise signal was decomposed using the EEMD algorithm to obtain the IMF components, and then the correlation coefficient method was used to determine the noise signal and the effective signal. Then, the RFOA was used to determine the noise signal IMF component threshold. Finally, the sample entropy of the obtained IMF components was used as a feature vector input of the GRNN algorithm for vibration mode recognition. Compared with the wavelet threshold method and the EEMD-GA method, the results show that the proposed algorithm has the highest signal-to-noise ratio and the best denoising effect.

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针对EEMD在水电机组振动信号降噪处理中的不足，提出一种基于改进果蝇算法（RFOA）优化EEMD噪声IMF分量阈值的降噪算法。通过EEMD算法将噪声信号分解，得到IMF分量，进而通过相关系数法确定噪声信号与有效信号，利用RFOA确定噪声信号IMF分量阈值；将求得的IMF分量的样本熵当作特征向量输入GRNN算法中，进行振动模式识别。研究结果表明，与小波阈值法、EEMD-GA方法相比，所提算法降噪时信噪比最高，降噪效果最佳。

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朱霄珣（1985-），男，博士、讲师、博导，研究方向为风功率预测，电网暂态故障影响及汽轮机故障检测，风电机传动系统故障诊断，信号处理及深度学习智能诊断等，E-mail：
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董利江（1986-），男，高级工程师，研究方向为能源与动力、信号处理，E-mail：

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董利江（1986-），男，高级工程师，研究方向为能源与动力、信号处理，E-mail：

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董利江（1986-），男，高级工程师，研究方向为能源与动力、信号处理，E-mail：

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RFOA优化EEMD阈值和SampEn的水电机组振动信号重构与特征提取
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董利江 1, 2 , 朱霄珣 3 , 刘伟 2 , 杨春旭 2 , 林翔 2 , 高晓霞 3 , 吕朝阳 3 , 胡乔良 2 , 苏海鹏 2
水电能源科学 | 机电与控制工程 2023,41(11): 178-182
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水电能源科学 | 机电与控制工程 2023, 41(11): 178-182
RFOA优化EEMD阈值和SampEn的水电机组振动信号重构与特征提取
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董利江1, 2 , 朱霄珣3 , 刘伟2, 杨春旭2, 林翔2, 高晓霞3, 吕朝阳3, 胡乔良2, 苏海鹏2
作者信息
  • 1.新疆新能集团有限责任公司乌鲁木齐电力建设调试所，新疆 乌鲁木齐 830011
  • 2.国网新疆电力有限公司电力科学研究院能源技术研究所，新疆 乌鲁木齐 830011
  • 3.华北电力大学动力工程系，河北 保定 071003

    • 董利江（1986-），男，高级工程师，研究方向为能源与动力、信号处理，E-mail：

通讯作者:

朱霄珣（1985-），男，博士、讲师、博导，研究方向为风功率预测，电网暂态故障影响及汽轮机故障检测，风电机传动系统故障诊断，信号处理及深度学习智能诊断等，E-mail：
Vibration Feature Extraction for Hydropower Units Based on RFOA Optimized Ensemble Empirical Mode Decomposition Threshold and Sample Entropy
Li-jiang DONG1, 2 , Xiao-xun ZHU3 , Wei LIU2, Chun-xu YANG2, Xiang LIN2, Xiao-xia GAO3, Zhao-yang LV3, Qiao-liang HU2, Hai-peng SU2
Affiliations
  • 1.Urumqi Electric Power Construction and Commissioning Institute of Xinjiang Xinneng Group, Urumqi 830011, China
  • 2.Institute of Energy Technology, Electric Power Research Institute of State Grid Xinjiang Electric Power, Urumqi 830011, China
  • 3.Power Engineering Department, North China Electric Power University, Baoding 071003, China
出版时间: 2023-11-25 doi: 10.20040/j.cnki.1000-7709.2023.20222298
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摘要
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针对EEMD在水电机组振动信号降噪处理中的不足，提出一种基于改进果蝇算法（RFOA）优化EEMD噪声IMF分量阈值的降噪算法。通过EEMD算法将噪声信号分解，得到IMF分量，进而通过相关系数法确定噪声信号与有效信号，利用RFOA确定噪声信号IMF分量阈值；将求得的IMF分量的样本熵当作特征向量输入GRNN算法中，进行振动模式识别。研究结果表明，与小波阈值法、EEMD-GA方法相比，所提算法降噪时信噪比最高，降噪效果最佳。

振动信号提取  /  集合经验模态分解  /  样本熵  /  特征提取  /  广义回归神经网络模型

Aiming at the shortcomings of hydropower unit vibration signal denoising using ensemble empirical mode decomposition (EEMD), a denoising algorithm based on an improved fruit fly optimization algorithm (RFOA) for optimizing the EEMD noise IMF component threshold was proposed. Firstly, the noise signal was decomposed using the EEMD algorithm to obtain the IMF components, and then the correlation coefficient method was used to determine the noise signal and the effective signal. Then, the RFOA was used to determine the noise signal IMF component threshold. Finally, the sample entropy of the obtained IMF components was used as a feature vector input of the GRNN algorithm for vibration mode recognition. Compared with the wavelet threshold method and the EEMD-GA method, the results show that the proposed algorithm has the highest signal-to-noise ratio and the best denoising effect.

extraction of vibration signal  /  ensemble empirical mode decomposition  /  sample entropy  /  feature extraction  /  generalized regression neural network model
董利江, 朱霄珣, 刘伟, 杨春旭, 林翔, 高晓霞, 吕朝阳, 胡乔良, 苏海鹏. RFOA优化EEMD阈值和SampEn的水电机组振动信号重构与特征提取. 水电能源科学, 2023 , 41 (11) : 178 -182 . DOI: 10.20040/j.cnki.1000-7709.2023.20222298
Li-jiang DONG, Xiao-xun ZHU, Wei LIU, Chun-xu YANG, Xiang LIN, Xiao-xia GAO, Zhao-yang LV, Qiao-liang HU, Hai-peng SU. Vibration Feature Extraction for Hydropower Units Based on RFOA Optimized Ensemble Empirical Mode Decomposition Threshold and Sample Entropy[J]. Water Resources and Power, 2023 , 41 (11) : 178 -182 . DOI: 10.20040/j.cnki.1000-7709.2023.20222298
基金
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  • 重庆市科学基金重点项目(035679)
  • 2002年高等学校博士学科点专项科研项目(20020183061)
2023年第41卷第11期
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doi: 10.20040/j.cnki.1000-7709.2023.20222298
  • 接收时间：2022-11-02
  • 首发时间：2026-01-27
  • 出版时间：2023-11-25
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  • 收稿日期：2022-11-02
  • 修回日期：2023-02-24
基金
重庆市科学基金重点项目(035679)
2002年高等学校博士学科点专项科研项目(20020183061)
作者信息
    1.新疆新能集团有限责任公司乌鲁木齐电力建设调试所，新疆 乌鲁木齐 830011
    2.国网新疆电力有限公司电力科学研究院能源技术研究所，新疆 乌鲁木齐 830011
    3.华北电力大学动力工程系，河北 保定 071003

通讯作者:

朱霄珣（1985-），男，博士、讲师、博导，研究方向为风功率预测，电网暂态故障影响及汽轮机故障检测，风电机传动系统故障诊断，信号处理及深度学习智能诊断等，E-mail：
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2种不同金属材料的力学参数
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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