EMD辅助相关系数SVD的单向阀故障诊断
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摘要
单向阀是往复式高压隔膜泵的关键部件,其故障振动信号常遭受强噪声污染,导致故障特征难以检测。针对这一问题,提出一种经验模态分解(EMD)辅助相关系数奇异值分解(SVD)的单向阀故障诊断方法。该方法首先将单向阀振动信号进行EMD分解,并将分解得到的本征模态函数(IMF)进行重构;然后将重构信号输入到相关系数SVD系统中进行二次分解,并用相关系数法筛选出包含故障特征信息的分量信号;最后对有效分量信号进行希尔伯特包络谱分析,实现单向阀故障诊断。仿真结果表明,提出方法解决了强噪声背景下故障特征提取困难的问题;实测数据表明,该方法能够有效检测出单向阀故障。
Check valve is a key component of the reciprocating high-pressure diaphragm pump. Regarding check valve fault vibration signal is often subjected to strong noise; it is difficult to detect the fault feature. A novel method to diagnosis the fault of check valve using empirical mode decomposition(EMD) and auxiliary correlation coefficient singular value decomposition(SVD) is proposed in this paper. Firstly, the check valve vibration signal is decomposed by empirical mode decomposition, then the intrinsic mode function(IMF) obtained by decomposition are used to reconstructed. Secondly, the reconstructed signal enters into the correlation coefficient SVD system for secondary decomposition and the component signals containing fault characteristic information are screened out by correlation coefficient method. Finally, the effective component signal is analyzed by Hilbert envelope spectrum analysis to diagnose the faulty of check valve. The simulation results show that the proposed method solved the problem of fault feature extraction under strong noise background, and the data measured indicates that the method can effectively detect the fault of the check valve.
Check valve is a key component of the reciprocating high-pressure diaphragm pump. Regarding check valve fault vibration signal is often subjected to strong noise; it is difficult to detect the fault feature. A novel method to diagnosis the fault of check valve using empirical mode decomposition(EMD) and auxiliary correlation coefficient singular value decomposition(SVD) is proposed in this paper. Firstly, the check valve vibration signal is decomposed by empirical mode decomposition, then the intrinsic mode function(IMF) obtained by decomposition are used to reconstructed. Secondly, the reconstructed signal enters into the correlation coefficient SVD system for secondary decomposition and the component signals containing fault characteristic information are screened out by correlation coefficient method. Finally, the effective component signal is analyzed by Hilbert envelope spectrum analysis to diagnose the faulty of check valve. The simulation results show that the proposed method solved the problem of fault feature extraction under strong noise background, and the data measured indicates that the method can effectively detect the fault of the check valve.
引文
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[16] Wang C H,Liu J,Min W W,et al.A novel sparse penalty for singular value decomposition[J].Chinese Journal of Electronics,2017,26(2):306-312
[17] 刘鎏,闫云聚,李鹏博.奇异谱分解在超声速无人机声振试验数据处理中的应用[J].振动与冲击,2015,34(3):28-34Liu L,Yan Y J,Li P B.Singular value spectral decomposition and its application in acoustic vibration test data processing of a supersonic aircraft[J].Journal of Vibration and Shock,2015,34(3):28-34 (in Chinese)
[18] 刘惟信.汽车设计[M].北京:清华大学出版社,2001:350-379Liu W X.Automobile design[M].Beijing:Tsinghua University Press,2001:350-379 (in Chinese)
[19] 赵志宏,杨绍普,申永军.基于独立分量分析与相关系数的机械故障特征提取[J].振动与冲击,2013,32(6):67-72Zhao Z H,Yang S P,Shen Y J.Machinery fault feature extraction based on independent component analysis and correlation coefficient[J].Journal of Vibration and Shock,2013,32(6):67-72 (in Chinese)
[20] 李洋,焦宗夏,吴帅.应用单向阀配流的高频往复泵的流量特性分析及优化设计[J].机械工程学报,2013,49(14):154-163Li Y,Jiao Z X,Wu S.Flow characteristics analysis and optimization design of high frequency reciprocation pump applying check valve to rectification[J].Journal of Mechanical Engineering,2013,49(14):154-163 (in Chinese)
[2] Colominas M A,Schlotthauer G,Torres M E.Improved complete ensemble EMD:a suitable tool for biomedical signal processing[J].Biomedical Signal Processing and Control,2014,14:19-29
[3] Smith J S.The local mean decomposition and its application to EEG perception data[J].Journal of the Royal Society Interface,2005,2(5):443-454
[4] Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London.Series A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995
[5] Bao F,Wang X L,Tao Z Y,et al.EMD-based extraction of modulated cavitation noise[J].Mechanical Systems and Signal Processing,2010,24(7):2124-2136
[6] 蒋永华,李荣强,焦卫东,等.应用EMD和双谱分析的故障特征提取方法[J].振动、测试与诊断,2017,37(2):338-342Jiang Y H,Li R Q,Jiao W D,et al.Feature extraction method based on empirical mode decomposition and bispectrum analysis[J].Journal of Vibration,Measurement & Diagnosis,2017,37(2):338-342 (in Chinese)
[7] 隋文涛,张丹,Wang W.基于EMD和MKD的滚动轴承故障诊断方法[J].振动与冲击,2015,34(9):55-59,64Sui W T,Zhang D,Wang W.Fault diagnosis of rolling element bearings based on EMD and MKD[J].Journal of Vibration and Shock,2015,34(9):55-59,64 (in Chinese)
[8] Qiao Z J,Pan Z R.SVD principle analysis and fault diagnosis for bearings based on the correlation coefficient[J].Measurement Science and Technology,2015,26(8):085014
[9] 张峻宁,张培林,华春蓉,等.改进K-SVD算法在曲轴轴承AE信号的去噪研究[J].振动与冲击,2017,36(21):150-156Zhang J N,Zhang P L,Hua C R,et al.Improved method for bearing AE signal denoising based on K-SVD algorithms[J].Journal of Vibration and Shock,2017,36(21):150-156 (in Chinese)
[10] Pan Z R,Qiao Z J.Feature extraction based on improved SVD de-noising and spectral kurtosis in early fault diagnosis of rolling element bearings[C]//Proceedings of the 5th International Symposium on Test Automation and Instrumentation.2014:14-21
[11] 蔡艳平,李艾华,石林锁,等.基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J].振动与冲击,2011,30(2):167-172,191Cai Y P,Li A H,Shi L S,et al.Roller bearing fault detection using improved envelope spectrum analysis based on EMD and spectrum kurtosis[J].Journal of Vibration and Shock,2011,30(2):167-172,191 (in Chinese)
[12] 程军圣,于德介,杨宇.EMD方法在转子局部碰摩故障诊断中的应用[J].振动、测试与诊断,2006,26(1):24-27Cheng J S,Yu D J.Yang Y.Application of EMD to local rub-impact fault diagnosis in rotor systems[J].Journal of Vibration,Measurement & Diagnosis,2006,26(1):24-27 (in Chinese)
[13] 赵学智,向可,叶邦彦,等.基于二次样条小波细节信号峰值的有效奇异值确定[J].振动与冲击,2010,29(11):6-12Zhao X Z,Xiang K,Ye B Y,et al.Determination of effective singular values based on detailed signal peak with quadratic spline wavelet[J].Journal of Vibration and Shock,2010,29(11):6-12 (in Chinese)
[14] Zhang S B,Lu S L,He Q B,et al.Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis[J].Journal of Sound and Vibration,2016,379:213-231
[15] 陈恩利,张玺,申永军,等.基于SVD降噪和盲信号分离的滚动轴承故障诊断[J].振动与冲击,2012,31(23):185-190Chen E L,Zhang X,Shen Y J,et al.Fault diagnosis of rolling bearings based on SVD denoising and blind signals separation[J].Journal of Vibration and Shock,2012,31(23):185-190 (in Chinese)
[16] Wang C H,Liu J,Min W W,et al.A novel sparse penalty for singular value decomposition[J].Chinese Journal of Electronics,2017,26(2):306-312
[17] 刘鎏,闫云聚,李鹏博.奇异谱分解在超声速无人机声振试验数据处理中的应用[J].振动与冲击,2015,34(3):28-34Liu L,Yan Y J,Li P B.Singular value spectral decomposition and its application in acoustic vibration test data processing of a supersonic aircraft[J].Journal of Vibration and Shock,2015,34(3):28-34 (in Chinese)
[18] 刘惟信.汽车设计[M].北京:清华大学出版社,2001:350-379Liu W X.Automobile design[M].Beijing:Tsinghua University Press,2001:350-379 (in Chinese)
[19] 赵志宏,杨绍普,申永军.基于独立分量分析与相关系数的机械故障特征提取[J].振动与冲击,2013,32(6):67-72Zhao Z H,Yang S P,Shen Y J.Machinery fault feature extraction based on independent component analysis and correlation coefficient[J].Journal of Vibration and Shock,2013,32(6):67-72 (in Chinese)
[20] 李洋,焦宗夏,吴帅.应用单向阀配流的高频往复泵的流量特性分析及优化设计[J].机械工程学报,2013,49(14):154-163Li Y,Jiao Z X,Wu S.Flow characteristics analysis and optimization design of high frequency reciprocation pump applying check valve to rectification[J].Journal of Mechanical Engineering,2013,49(14):154-163 (in Chinese)