[1]吕明珠 苏晓明 陈长征 刘世勋.小波包能量熵与EMD结合分析法在风机滚动轴承故障诊断中的应用[J].机械与电子,2018,(06):8-12.
　LYU Mingzhu,SU Xingming,CHEN Changzheng,et al.Application of Wavelet Packet Energy Entropy and EMD Conjoint Analysis in Fault Diagnosis of Wind Turbine Bearing[J].Machinery & Electronics,2018,(06):8-12.
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小波包能量熵与EMD结合分析法在风机滚动轴承故障诊断中的应用()
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机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2018年06期
页码:
8-12
栏目:
设计与研究
出版日期:
2018-06-24

文章信息/Info

Title:
Application of Wavelet Packet Energy Entropy and EMD Conjoint Analysis in Fault Diagnosis of Wind Turbine Bearing
文章编号:
1001-2257（2018)06-0008-05
作者:
吕明珠1; 2 苏晓明 1 陈长征 1 刘世勋3
(1.沈阳工业大学机械工程学院,辽宁 沈阳 110870; 2.辽宁装备制造职业技术学院自动控制工程学院,辽宁 沈阳 110161; 3.中认(沈阳)北方实验室有限公司，辽宁 沈阳 110164）
Author(s):
LYU Mingzhu1; 2;  SU Xingming1;  CHEN Changzheng1;  LIU Shixun3
(1. School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110870, China; 2. Department of Automatic Control, Liaoning Equipment Manufacturing Professional Technology Institute, Shenyang 110161, China; 3. CQC (Shenyang) North Laboratory, Shenyang 110164, China)
关键词:
风力发电机; 滚动轴承; 故障诊断; 小波包分解; 能量熵; EMD
Keywords:
wind turbine;  rolling bearing;  fault diagnosis;  wavelet packet decomposition;  energy entropy;  EMD
分类号:
TH133
文献标志码:
A
摘要:
针对风力发电机组的驱动端滚动轴承故障率高，单一的振动信号分析方法难以实现大量状态信息的有效提取和准确的状态监测诊断等问题，提出了一种将小波包能量熵与EMD结合的故障诊断方法。通过小波包分解和设置阈值，有效消除了噪声对原始振动信号的影响，以能量熵值为指标描述振动信号能量分布的变化，然后采用EMD方法和相关性系数计算将最能体现振动特征的IMF分量分离出来，再经过Hilbert变换和FFT变换得到包络谱，将时域信号变换到频域上，有效提取了风力发电机组驱动端滚动轴承的故障特征频率，准确诊断出故障所在的位置。最后，通过拆机结果验证了该诊断方法的正确性。
Abstract:
The driving end rolling bearing of the wind turbine has high failure rate, and the single vibration signal analysis method cannot effectively extract a large amount of state information and accurately diagnose the failure. Aiming at these problems, this paper proposes a fault diagnosis method combining wavelet packet energy entropy with EMD. First, the influence of noise on the original vibration signal was effectively eliminated by using wavelet packet decomposition and setting threshold, and then, the change of energy distribution of the vibration signal was described by energy entropy. Next, the EMD method and the correlation coefficient computation were used to separate the IMF component which can reflect the vibration characteristics. After that, the envelope spectrum was obtained by Hilbert and FFT transform and the time domain signal was transformed into the frequency domain. In this way, the fault characteristics frequency of the driving end rolling bearing of the wind turbine can be effectively extracted and the position of the fault can be accurately diagnosed. Finally, the disassemble experimental results verify the correctness of the diagnosis method

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更新日期/Last Update: 2019-10-30