基于改进VMD和深度置信网络的风机易损部件故障预警

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摘要考虑到风电机组运行时监测到的轴承、齿轮等易损部件的振动信号早期故障特征微弱且难以提取，提出了基于变分模态分解的风机易损部件故障特征提取方法，并采用深度置信网络对故障进行预警。为克服变分模态分解参数选取对特征提取效果的影响，基于各分量的相关系数确定分解个数，并采用粒子群算法来优化惩罚因子，将改进的变分模态分解用于振动信号进行分析处理，在此基础上，进一步提取各分量的排列熵和均方根值并将其构成的高维特征向量作为深度置信网络的输入，建立早期故障诊断模型。最后选取风机传动故障诊断实验平台早期故障数据和某风电机组的现场信号进行故障诊断分析，结果表明，该方法能准确稳定地提取风机易损部件故障信号的微弱特征，并进行故障有效识别，提高了风机易损部件故障预警的准确性。

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关键词 ：变分模态分解, 多特征提取, 深度置信网络, 故障诊断

Abstract：Considering that the early fault characteristics of the vibration signals of the vulnerable components such as bearings and gears monitored during the operation of wind turbines are weak and difficult to extract, a fault feature extraction method based on VMD was proposed.The deep belief network was used to troubleshoot the faults.In order to overcome the influence of the parameters of the variational mode decomposition on the feature extraction, the number of decompositions was determined based on the correlation coefficients of each component, and the particle swarm optimization algorithm was used to optimize the penalty factor.The improved variational mode decomposition was applied to the vibration signals analysis and processing.Based on this, the permutation entropy and rms value of each modal component were further extracted and the high-dimensional eigenvectors formed by them were used as the input of the deep beilef network to establish an early fault diagnosis model.Finally, fault diagnosis and analysis of wind turbine drive fault diagnosis experimental platform early fault data and an offshore wind turbine site signal were carried out.The results show that the method can extract the weak features of fault signals of fan vulnerable components more accurately and steadily.

Key words： variational mode decomposition multi-feature extraction deep belief network fault diagnosis

收稿日期: 2017-11-10 出版日期: 2019-04-15

引用本文:

郑小霞1，陈广宁1，任浩翰2，李东东1. 基于改进VMD和深度置信网络的风机易损部件故障预警[J]. 振动与冲击, 2019, 38(8): 153-160.
ZHENG Xiaoxia1 CHEN Guangning1 REN Haohan2 LI Dongdong1 . Fault detection of vulnerable units of wind turbine based on improved VMD and DBN. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(8): 153-160.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I8/153

[1] 唐贵基，庞彬，刘尚坤. 基于奇异差分谱和平稳子空间分析的滚动轴承故障诊断[J]. 振动与冲击，2015(11):83-87.
TANG Gui-ji, PANG Bin, LIU Shangkun. Fault diagnosis of rolling bearings based on difference spectrum of singular value and stationary subspace analysis [J]. Journal of Vibration & Shock, 2015, 34(11):83-87.
[2] Dragomiretskiy K， Zosso D.Variational mode decomposition [J]. IEEE Tran on Signal Processing,2014，62(3):531-544．
[3] 刘长良，武英杰，甄成刚. 基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J]. 中国电机工程学报，2015,35(13):3358-3365.
LIU Changliang, WU Yingjie, ZHEN Chenggang. Rolling Bearing Fault Diagnosis Based on Variational Mode Decomposition and Fuzzy C Means Clustering [J]. Proceedings of the CSEE ,2015,35(13):3358-3365.
[4] 唐贵基，王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报， 2015, 49(5):73-81.
TANG Gui-ji, WANG Xiao-long. Parameter Optimized Variational Mode Decomposition Method with Application to Incipient Fault Diagnosis of Rolling Bearing [J]. Journal of Xi’an Jiaotong university. 2015, 49(5):73-81.
[5] 赵洪山，郭双伟，高夺. 基于奇异值分解和变分模态分解的轴承故障特征提取[J]. 振动与冲击,2016,35(22):183-188.
ZHAO Hongshan,GUO Shuangwei,GAO Duo. Fault feature extraction of bearing faults based on singular value decomposition and variational modal decomposition[J]. Journal of Vibration and Shock,2016,35(22):183-188.
[6] Hinton G，Osindero S，Teh Y W.A fast learning algorithm for deep belief nets [J].Neural Computation,2006，18(7):1527-1554
[7] 李艳峰，王新晴，张梅军，等. 基于奇异值分解和深度信度网络多分类器的滚动轴承故障诊断方法[J]. 上海交通大学学报，2015,49(05):681-686.
LI Yanfeng,WANG Xinqing,ZHANG Meijun,et al.An Approach to Fault Diagnosis of Rolling Bearing Using SVD and Multiple DBN Classifiers [J]. Journal of Computer Research and Development ,2015,49(05):681-686.
[8] 李巍华，单外平，曾雪琼. 基于深度信念网络的轴承故障分类识别[J]. 振动工程学报,2016,29(02):340-347.
LI Wei-hua,SHAN Wai-ping,ZENG Xue-qiong. Bearing fault identification based on deep belief network[J]. Journal of Vibration Engineering,2016,29(02):340-347.
[9] 朱可恒.滚动轴承振动信号特征提取及诊断方法研究[D].大连理工大学，2013.
ZHU Keheng.Research on Vibration Signal based Rolling Element Bearing Feature Extraction and Fault Diagnosis Method [D].Dalian University of Technology,2013.
[10] 唐贵基，王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报， 2015, 49(5):73-81.
TANG Gui-ji,WANG Xiaolong.Parameter Optimized Variational Mode Decomposition Method with Application to Incipient Fault Diagnosis of Rolling Bearing[J].Journal of Xi’an Jiaotong University, 2015, 49(5):73-81.
[11] Amin Ghodousian，Maryam Raeisian Parvari. A modified PSO algorithm for linear optimization problem subject to the generalized fuzzy relational inequalities with fuzzy constraints [J]. Information Sciences,2017,418-419.
[12] Jia R S，Liang Y Q，Hua Y C,et al. Suppressing non-stationary random noise in microseismic data by using ensemble empirical mode decomposition and permutation entropy[J]. Journal of Applied Geophysics, 2016, 133:132-140.
[13] Yan R，Liu Y，Gao R X. Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines [J]. Mechanical Systems & Signal Processing, 2012, 29(5):474-484.
[14] Zhang X，Liang Y，Zhou J. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM [J]. Measurement, 2015: 164-179.
[15] Christoph B，Bernd P. Permutation entropy: a natural complexity measure for time series [J]. Physical Review Letters, 2002, 88(17):174102.
[16] F.A. Donoso， K.J. Austin，P.R. McAree. Three new Iterative Closest Point variant-methods that improve scan matching for surface mining terrain[J]. Robotics & Autonomous Systems, 2017,95:117-128.