[1]李 娜,曹丽明.一种风力发电机轴承故障智能诊断方法[J].工业仪表与自动化装置,2022,(05):103-108.[doi:10.19950/j.cnki.cn61-1121/th.2022.05.019]
　LI Na,CAO Liming.An intelligent diagnosis method for wind turbine bearing fault[J].Industrial Instrumentation & Automation,2022,(05):103-108.[doi:10.19950/j.cnki.cn61-1121/th.2022.05.019]
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一种风力发电机轴承故障智能诊断方法

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2022年05期
页码:
103-108
栏目:
出版日期:
2022-10-15

文章信息/Info

Title:
An intelligent diagnosis method for wind turbine bearing fault
文章编号:
1000-0682（2022）05-0000-00
作者:
李 娜; 曹丽明
中车永济电机有限公司，陕西 西安 710000
Author(s):
LI Na; CAO Liming
CRRC Yong Ji Electric Co.Ltd., Shaanxi Xi’an 710000,China
关键词:
轴承; 故障诊断; 卷积神经网络; 风力发电机; 声音信号
Keywords:
bearing;  fault diagnosis;  convolutional neural network;  wind turbines;  sound signal
分类号:
TH165.3
DOI:
10.19950/j.cnki.cn61-1121/th.2022.05.019
文献标志码:
A
摘要:
准确识别轴承故障是提高双馈风力发电机运行稳定性的重要手段，声音信号包含大量设备运行状态信息。该文人为预制4种不同的轴承故障，并将故障轴承安装在风力发电机上，在4种转速下对不同故障轴承的运行声音信号进行采集；对采集到的声音信号进行EMD分解，依据相关系数法选取分解的模态分量。基于混沌理论采用相空间重构法将一维数据序列转换为二维序列，利用CNN对二维序列进行特征提取与故障诊断。结果表明该方法对不同转速下滚动轴承故障能够实时准确识别。
Abstract:
Accurate identification of bearing faults is an important means to improve the operational stability of doubly-fed wind turbines, and sound signals contain a large amount of equipment operating status information. In this paper, four kinds of bearing faults are prefabricated artificially, the faulty bearing was installed on the wind turbine. The operating sound signals of the different faulty bearings were collected at four rotational speeds. The sound signal decomposed by EMD and the IMFS were selected according to the correlation coefficient method. Based on the chaos theory, the two-dimensional sequence were built by phase space reconstruction method.The intelligent fault diagnosis system was built by CNN. The results show that the method can more efficient and accurately identify the faults of rolling bearings at different speeds.

参考文献/References:

[1] 雷子豪,温广瑞,周桥,等.基于MMDFE-DA的滚动轴承故障诊断方法[J].振动.测试与诊断,2022,42(01):182-189+203.

[2] R Fang,R Shang,S Jiang.A trend cloud model-based approach for the identification of wind turbine gearbox anomalies Wang Gongxian[J].Journal of Intelligent & Fuzzy System,2018,35(1):415-421.
[3] 郝勇,吴文辉,商庆园.基于变分模态分解和支持向量机的滚动轴承品质评估[J].控制理论与应用,2020,37(07):1544- 1551.
[4] 刘飞,陈仁文,邢凯玲,等.基于迁移学习与深度残差网络的滚动轴承快速故障诊断算法[J].振动与冲击,2022,41(03): 154-164.?/div>
[5] 金江涛,许子非,李春,等.基于深度学习与混沌特征融合的滚动轴承故障诊断[J].控制理论与应用,2022,39(01): 109-116.
[6] 向玲,王朋鹤,李京蓄.基于CNN-LSTM的风电机组异常状态检测[J].振动与冲击,2021,40(22):11-17.
[7] XU X J,ZHAO Z Z,XU X B,et al.Machine learning- based wear fault diagnosis for marine diesel engine by fusing multiple datadriven models[J].Knowledge-based Systems,2020,190: 105324.
[8] CHE C,WANG H,NI X,et al.Hybrid multimodal fusion with deep learning for rolling bearing fault diagnosis[J]. Measurement,2020,173(3):10865.
[9] 赵小强,张亚洲.利用改进卷积神经网络的滚动轴承变工况故障诊断方法[J].西安交通大学学报,2021,55(12):108-118.
[10] 谷玉海,朱腾腾,饶文军,等.基于EMD二值化图像和CNN的滚动轴承故障诊断[J].振动.测试与诊断,2021,41(01): 105-113+203.
[11] 朵慕社,纪国宜,朱海龙,等.基于VMD降噪和CNN的轴承故障诊断[J].噪声与振动控制,2021,41(05):155-160.
[12] 龙磊,何兵,刘方,等.基于KJADE的列车轴承轨边声学诊断方法研究[J].振动.测试与诊断,2020,40(04):781-787+ 828-829.?div>[13] 申博文,王华庆,唐刚,等.基于MCKD与CEEMDAN的声信号故障特征提取方法[J].复旦学报(自然科学版),2019, 58(03):385-392+400.
[14] 永远,黄小霞,唐媛恬.基于时频域特征分析的列车轴承缺陷实时检测[J].西南交通大学学报,2017,52(06): 1182-1187.
[15] 刘方,沈长青,何清波,等.基于时域多普勒校正和EEMD的列车轴承道旁声音监测故障诊断方法研究[J].振动与冲击,2013,32(24):104-109.
[16] 宋锐.基于混沌激励的结构损伤识别研究[D].南京:东南大学, 2015.
[17] 王德丽,韩宝珠,佟庆彬.基于RSGWPT和EEMD的滚动轴承故障诊断[J].电子设计工程,2016,(11):102-104.
[18] 刘东瀛,邓艾东,刘振元.基于EMD与相关系数原理的故障声发射信号降噪研究[J].振动与冲击,2017,36(19):71-77.

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备注/Memo

备注/Memo:
收稿日期：2022-04-24

作者简介：
李娜（1986），女，陕西西安人，硕士研究生，工程师，研究方向为机电装备的故障智能诊断系统研究。
更新日期/Last Update: 1900-01-01