最优品质因子信号共振稀疏分解的往复压缩机故障诊断
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摘要
针对往复压缩机振动信息干扰耦合,振动信号非平稳、非线性的特性,提出了最优品质因子信号共振稀疏分解的往复压缩机故障诊断方法。该方法以信号共振稀疏分解得到的低共振分量峭度最大为目标,利用遗传算法与粒子群算法结合的混合算法对品质因子进行优化,得到最优品质因子;然后利用最优品质因子对往复压缩机振动信号进行信号共振稀疏分解,提取故障信息。实验及结果表明,该方法在往复压缩机轴承故障诊断方面效果显著。
Reciprocating compressor vibration signal is typical nonlinear and non-stationary,and the vibration information interference coupling, owing to this problem,a fault diagnosis method of reciprocating compressor on the resonance-based sparse signal decomposition with optimal Q-factor was proposed.The method use resonance sparse decomposition to find the low resonance component which its kurtosis is maximum, optimize Q-factor with genetic algorithm and particle swarm optimization to get the optimal Q-factor;then use resonance sparse decomposition to decompose reciprocating compressor vibration signal by the optimal Q-factor;the result shows that this method can diagnose the oversized bearing clearance fault effectively.
Reciprocating compressor vibration signal is typical nonlinear and non-stationary,and the vibration information interference coupling, owing to this problem,a fault diagnosis method of reciprocating compressor on the resonance-based sparse signal decomposition with optimal Q-factor was proposed.The method use resonance sparse decomposition to find the low resonance component which its kurtosis is maximum, optimize Q-factor with genetic algorithm and particle swarm optimization to get the optimal Q-factor;then use resonance sparse decomposition to decompose reciprocating compressor vibration signal by the optimal Q-factor;the result shows that this method can diagnose the oversized bearing clearance fault effectively.
引文
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[14] 黄文涛,付强,窦宏印.基于自适应优化品质因子的共振稀疏分解方法及其在行星齿轮箱复合故障诊断中的应用[J].机械工程学报,2016,52(15):44-51.HUANG WenTao,FU Qiang,DOU HongYin.Resonance-based sparse signal decomposition based on the qualityfactors optimization and its application of composite fault diagnosis to planetary gearbox[J].Journal of Mechanical Engineering,2016,52(15):44-51(In Chinese).
[15] 陈向民,于德介,罗洁思.基于信号共振稀疏分解的转子早期碰摩故障诊断方法[J].中国机械工程,2013,1(8):35-41.CHEN XiangMin,YU DeJie,LUO JieSi.Early rub-impact diagnosis of rotors by using resonance-based sparse signal decomposition[J].China Mechanical Engineering,2013,1(8):35-41(In Chinese).
[16] Afonso M V,Bioucas J M,Figueiredo M A T.An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems[J].IEEE Transactions on Image Processing,2011,20(3):681-695.
[17] 任会礼,王冲,钟懿,等.基于改进粒子群算法的高强钢Y-U本构模型参数反演[J].机械强度,2014,36(04):527-531.REN HuiLi,WANG Chong,ZHONG Yi,et al.Y-U Constitutive model parameters inversion of high strength steel based on modified particle swarm optimization algorithm[J].Journal of Mechanical Strength,2014,36(4):527-531(In Chinese).
[2] Selesnick I W.Resonance-based signal decomposition:A new sparsity-enabled signal analysis method[J].Signal Processing,2011,91(12):2793-2809.
[3] Selesnick I W.Wavelet transform with tunable Q-fac-tor[J].IEEE Transactions on Signal Processing,2011,59(8):3560-3575.
[4] 肖顺根,宋萌萌,赖联锋.自适应冗余提升小波包变换的滚动轴承故障诊断新方法[J].机械强度,2015,37(5):816-822.XIAO ShunGen SONG MengMeng LAI LianFeng.Fault diagnosis new method of rolling beaing based on adaptive edundant lifting scheme packeT[J].Journal of Mechanical Strength,2015,37(5):816-822(In Chinese).
[5] 张文义,于德介,陈向民.基于信号共振稀疏分解与包络谱的齿轮故障诊断[J].中国机械工程,2013,24(24):3349-3354.ZHANG WenYi,YU DeJie,CHEN Xiang-min.Fault Diagnosis of gears based on resonance-based sparse signal decomposition and envelope spectrum[J].China Mechanical Engineering,2013,24(24):3349-3354(In Chinese).
[6] 李星,于德介,张顶成.基于最优品质因子信号共振稀疏分解的滚动轴承故障诊断[J].振动工程学报,2015,28(6):998-1005.LI Xing,YU DeJie,ZHANG DingCheng.Fault diagnosis of rolling bearings based on the resonance-based sparse signal decomposition with optimal Q-Factor[J].Journal of Vibration Engineering,2015,28(6):998-1005(In Chinese).
[7] 张顶成,于德介,李星.滚动轴承故障诊断的品质因子可调小波重构方法[J].航空动力学报,2015,30(12):3051-3057.ZHANG DingCheng,YU DeJie,LI Xing.Fault diagnosis of rolling bearings based on tunable-Q wavelet reconstruction[J].Journal of Aerospace Power,2015,30(12):3051-3057(In Chinese).
[8] 莫代一,崔玲丽,王婧.基于双重Q因子的稀疏分解法在滚动轴承早期故障诊断中的应用[J].机械工程学报,2013,49(9):37-41.MO DaiYi,CUI LingLi,WANG Qian.Sparse signal decomposition method based on the dual Q-factor and its application to rolling bearing early fault diagnosis[J].Journal of Mechanical Engineering,2013,49(9):37-41(In Chinese).
[9] 余发军,周凤星,严保康.基于字典学习的轴承早期故障稀疏特征提取[J].振动与冲击,2016,35(6):181-186.YU FaJun,ZHOU FengXing,YAN BaoKang.Bearing initial fault feature extraction via sparse representation based on dictionary learning[J].Journal of Vibration and Shock,2016,35(6):181-186(In Chinese).
[10] 余发军,张新英,李伟锋,等.航空物流传送设备中轴承故障稀疏特征提取[J].计算机测量与控制,2015,23(9):3003-3008.YU FaJun,ZHANG XinYing,LI WeiFeng,et al.Sparse feature extraction of fsult bearing in aviation logistics transmission equipment[J].Computer Measurement & Control,2015,23(9):3003-3008(In Chinese).
[11] Holland J H.Adaption in nature and artificial systems[M].MIT Press,1992:132-140.
[12] 彭帅英,李广杰,彭文,等.基于改进遗传算法的Holt-Winters模型在采空沉陷预测中的应用[J].吉林大学学报(地球科学版),2013,43(2):515-520.PENG ShuaiYing,LI GuangJie,PENG Wen,et al.Mining subsidence forecast method based on improved genetic algorithm ang holt-winters model[J].Journal of Jilin University(Earth Science Edition),2013,43(2):515-520(In Chinese).
[13] 金敏,鲁华祥.一种遗传算法与粒子群优化的多子群分层混合算法[J].控制理论与应用,2013,30(10):1231-1238.JIN Min,LU HuaXiang.A multi-subgroup hierarchical hybrid of genetic algorithm andparticle swarm optimization[J].Control Theory & Applications,2013,30(10):1231-1238(In Chinese).
[14] 黄文涛,付强,窦宏印.基于自适应优化品质因子的共振稀疏分解方法及其在行星齿轮箱复合故障诊断中的应用[J].机械工程学报,2016,52(15):44-51.HUANG WenTao,FU Qiang,DOU HongYin.Resonance-based sparse signal decomposition based on the qualityfactors optimization and its application of composite fault diagnosis to planetary gearbox[J].Journal of Mechanical Engineering,2016,52(15):44-51(In Chinese).
[15] 陈向民,于德介,罗洁思.基于信号共振稀疏分解的转子早期碰摩故障诊断方法[J].中国机械工程,2013,1(8):35-41.CHEN XiangMin,YU DeJie,LUO JieSi.Early rub-impact diagnosis of rotors by using resonance-based sparse signal decomposition[J].China Mechanical Engineering,2013,1(8):35-41(In Chinese).
[16] Afonso M V,Bioucas J M,Figueiredo M A T.An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems[J].IEEE Transactions on Image Processing,2011,20(3):681-695.
[17] 任会礼,王冲,钟懿,等.基于改进粒子群算法的高强钢Y-U本构模型参数反演[J].机械强度,2014,36(04):527-531.REN HuiLi,WANG Chong,ZHONG Yi,et al.Y-U Constitutive model parameters inversion of high strength steel based on modified particle swarm optimization algorithm[J].Journal of Mechanical Strength,2014,36(4):527-531(In Chinese).
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