|本期目录/Table of Contents|

[1]郑小霞,叶聪杰,周荣成. 基于改进 DEMD 和 ICA 的海上风机传动系统早期故障诊断[J].电机与控制学报,2017,21(11):82-89.[doi:10.15938/j.emc.2017.11.011]
 ZHENG Xiao-xia,YE Cong-jie,ZHOU ong-cheng. Early fault diagnosis of offshore wind turbines transmission system based on improved DEMD and ICA[J].,2017,21(11):82-89.[doi:10.15938/j.emc.2017.11.011]
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 基于改进 DEMD 和 ICA 的海上风机传动系统早期故障诊断(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
21
期数:
2017年11
页码:
82-89
栏目:
出版日期:
2017-11-01

文章信息/Info

Title:
 Early fault diagnosis of offshore wind turbines transmission system based on improved DEMD and ICA
作者:
 郑小霞1 叶聪杰2 周荣成3
 1. 上海电力学院 自动化工程学院,上海 200090; 2. 国网浙江省电力公司金华供电公司,浙江 金华 321000; 3. 上海东海风力发电有限公司,上海 200090)
Author(s):
 ZHENG Xiao-xia1 YE Cong-jie2 ZHOU Rong-cheng3
 1. School of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China; 2. Jinhua Electric Power Supply Company,Jinhua 321000,China; 3. Shanghai Donghai Wind Power Co. ,Ltd. ,Shanghai 200090,China)
关键词:
海上风电 传动系统 早期故障诊断 经验模式分解 独立分量分析
Keywords:
offshore wind turbines transmission system early fault diagnosis empirical mode decomposition independent component analysis
分类号:
TM 307
DOI:
10.15938/j.emc.2017.11.011
文献标志码:
A
摘要:
 针对传动系统早期故障振动信号较弱的情况,提出基于改进微分经验模式分解( DEMD) 和独立分量分析( ICA) 的海上风机传动系统早期故障诊断方法。为克服传统的 DEMD 算法在分解低阶本征模态函数( IMF) 时存在失真现象,提出改进的微分经验模式算法将原始振动信号分解成若干个独立的 IMF 信号,结合 ICA 进一步进行原始振动信号故障特征分量的提取,并基于标准数据和风机动力传动故障诊断实验平台进行了仿真研究,最后选取海上风电机组传动系统常出现的发电机轴承故障进行诊断分析。结果表明,相对于传统的故障诊断方法,该方法能更好地放大故障分量,减少噪声和其他振动干扰信号的影响,提高了海上风电机组传动系统早期故障诊断的准确性。
Abstract:
 Regarding weak fault characteristic of transmission system,a new early fault diagnosis method based on improved differential empirical mode decomposition ( DEMD) and independent component analysis ( ICA) was proposed for offshore wind turbines transmission system. In view of the mode mixing phenomenon in signal decomposition for empirical mode decomposition,an improved differential empirical mode decomposition was presented to decompose the original vibration signal into several independent intrinsic mode functions( IMF) components,then ICA was used for extracting the dominating features from appropriate IMF components. The proposed algorithm was simulated through standard fault data and fault data of wind power transmission experimental platform. Finally,the fault diagnosis of the generator bearing fault which often appears in the offshore wind turbine transmission system was analyzed. The experimental results show that the proposed method can effectively enlarge the fault characteristics and improve the veracity of early diagnosis for offshore wind turbines transmission system.

参考文献/References:

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备注/Memo

备注/Memo:
 收稿日期: 2015 - 04 - 09
 基金项目: 国家自然科学基金( 51507098)
作者简介: 郑小霞( 1978—) ,女,博士,副教授,研究方向为风力发电故障诊断与运行维护; 叶聪杰( 1988—) ,男,硕士,研究方向为风机故障诊断; 周荣成( 1984—) ,男,学士,工程师,研究方向为风电场运行维护。
更新日期/Last Update: 2018-02-06