基于粒子群优化盲源分离方法的电机滚动轴承复合故障诊断
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摘要
基于粒子群算法实现容易、精度高、收敛快等优点,将粒子群算法与盲源分离相结合,提出基于粒子群优化的盲源分离方法 (PSO-BSS),并将其应用于电机滚动轴承复合故障诊断中。PSO-BSS方法以峭度的绝对值之和作为目标函数,通过PSO寻找到目标函数的最大值,进而确定最优的分离矩阵。仿真结果表明:PSO-BSS方法能够实现多源复合故障信号的特征分离,并且在分离性能、算法收敛性以及运算速度方面都明显地优于传统的基于遗传算法的机械故障盲源分离方法。最后成功地将PSO-BSS方法应用于实际的滚动轴承内、外圈复合故障盲源分离中,取得良好的分析效果,验证了该方法的工程实用性。
Based on the advantages of particle swarm optimization( PSO) that was easily to be realized,high precision and fast convergence,combining PSO and blind source separation( BSS),a new method of mechanical failure blind source separation based on PSO was proposed( PSO-BSS),and the method was applied to rolling bearing compound fault diagnosis. In the PSO-BSS method,the sum of the absolute value of the kurtosis was taken as the target function,the maximum of the target function was sought by PSO,then the optimal separation matrix was determined. The simulation results show that the PSO-BSS method can be used to separate the fault features of multi-pile compound fault signal and is significantly superior to the traditional mechanical failure blind source separation based on genetic algorithm( GA-BSS) in separation performance,algorithm convergence and operation speed. Finally,the PSOBSS method was successfully applied to the actual rolling bearing inner and outer mixing fault blind source separation,a good result was obtained which validated its engineering practicality.
Based on the advantages of particle swarm optimization( PSO) that was easily to be realized,high precision and fast convergence,combining PSO and blind source separation( BSS),a new method of mechanical failure blind source separation based on PSO was proposed( PSO-BSS),and the method was applied to rolling bearing compound fault diagnosis. In the PSO-BSS method,the sum of the absolute value of the kurtosis was taken as the target function,the maximum of the target function was sought by PSO,then the optimal separation matrix was determined. The simulation results show that the PSO-BSS method can be used to separate the fault features of multi-pile compound fault signal and is significantly superior to the traditional mechanical failure blind source separation based on genetic algorithm( GA-BSS) in separation performance,algorithm convergence and operation speed. Finally,the PSOBSS method was successfully applied to the actual rolling bearing inner and outer mixing fault blind source separation,a good result was obtained which validated its engineering practicality.
引文
[1]唐贵基,庞彬.基于1.5维Teager峭度谱的滚动轴承故障诊断研究[J].振动与冲击,2015,34(15):72-76.TANG G J,PANG B.Rolling Element Bearing Fault Diagnosis Based on 1.5-dimensional Teager Kurtosis Spectrum[J].Journal of Vibration and Shock,2015,34(15):72-76.
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[8]滕峰成,郝宇,林晓乐.基于PSO算法的MFF模型的参数辨识与优化[J].计量学报,2017,38(2):209-214.TENG F C,HAO Y,LIN X L.Parameter Identification and Optimization of the MFF Model Based on the Particle Swarm Optimization Algorithm[J].ACTA Metrologica Sinica,2017,38(2):209-214.
[9]谢习华,徐雷,谭耀,等.基于PSO-GRNN的直升机旋翼不平衡故障诊断[J].噪声与振动控制,2015,35(2):175-179.XIE X H,XU L,TAN Y,et al.Fault Diagnosis of Helicopter Rotor’s Unbalance Based on PSO-GRNN Algorithms[J].Noise and Vibration Control,2015,35(2):175-179.
[10]余梓唐.基于粒子群算法优化支持向量机汽车故障诊断研究[J].计算机应用研究,2012,29(2):572-574.YU Z T.Automotive Fault Diagnosis Based on SVM and Particle Swarm Algorithm[J].Application Research of Computers,2012,29(2):572-574.
[11]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 G J,WANG X L.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.
[12]张淑清,胡永涛,姜安琦,等.基于双树复小波和自适应权重和时间因子的粒子群优化支持向量机的轴承故障诊断[J].中国机械工程,2017,28(3):327-333.ZHANG S Q,HU Y T,JIANG A Q,et al.Bearing Fault Diagnosis Based on DTCWT and AWTFPSO-optimized SVM[J].China Mechanical Engineering,2017,28(3):327-333.
[13]陈法法,汤宝平,董绍江.基于粒子群优化LS-WSVM的旋转机械故障诊断[J].仪器仪表学报,2011,32(12):2747-2753.CHEN F F,TANG B P,DONG S J.Rotating Machinery Fault Diagnosis Based on LS-WSVM with Particle Swarm Optimization[J].Chinese Journal of Scientific Instrument,2011,32(12):2747-2753.
[14]毋文峰,陈小虎,苏勋家.基于经验模式分解的单通道机械信号盲分离[J].机械工程学报,2011,47(4):12-16.WU W F,CHEN X H,SU X J.Blind Source Separation of Signal-channel Mechanical Signal Based on Empirical Mode Decomposition[J].Journal of Mechanical Engineering,2011,47(4):12-16.
[15]HO D,RANDALL R B.Optimization of Bearing Diagnostic Technique Using Simulated and Actual Bearing Fault Signals[J].Mechanical System and Signal Processing,2000,14(5):763-788.
[2]胥永刚,孟志鹏,赵国亮.基于双树复小波变换的轴承复合故障诊断研究[J].仪器仪表学报,2014,35(2):447-452.XU Y G,MENG Z P,ZHAO G L.Study on Compound Fault Diagnosis of Rolling Bearing Based on Dual-tree Complex Wavelet Transform[J].Chinese Journal of Scientific Instrument,2014,35(2):447-452.
[3]李良敏.基于遗传算法的盲源分离在轴承诊断中的应用[J].轴承,2005(9):31-34.LI L M.Application of Blind Source Separation Method Based on Genetic Algorithm in Bearing Fault[J].Bearing,2005(9):31-34.
[4]ROJAS F,ROJAS I,CLEMENTE R M,et al.Nonlinear Blind Source Separation Using Genetic Algorithms[C]//Proceedings of International Conference on Independent Component Analysis,2001:400-405.
[5]LI M,MAO J.A New Algorithm of Evolutional Blind Source Separation Based on Genetic Algorithm[C]//Proceedings of Fifth World Congress on Intelligent Control and Automation,2004:2240-2244.
[6]KENNEDY J,EBERHART R.Particle Swarm Optimization[C]//Proceedings of IEEE International Conference on Neural Networks,1995:1942-1948.
[7]李良敏.基于遗传算法的盲源分离算法[J].西安交通大学学报,2005,39(7):740-743.LI L M.New Blind Source Separation Method Based on Genetic Algorithm[J].Journal of Xi’an Jiaotong University,2005,39(7):740-743.
[8]滕峰成,郝宇,林晓乐.基于PSO算法的MFF模型的参数辨识与优化[J].计量学报,2017,38(2):209-214.TENG F C,HAO Y,LIN X L.Parameter Identification and Optimization of the MFF Model Based on the Particle Swarm Optimization Algorithm[J].ACTA Metrologica Sinica,2017,38(2):209-214.
[9]谢习华,徐雷,谭耀,等.基于PSO-GRNN的直升机旋翼不平衡故障诊断[J].噪声与振动控制,2015,35(2):175-179.XIE X H,XU L,TAN Y,et al.Fault Diagnosis of Helicopter Rotor’s Unbalance Based on PSO-GRNN Algorithms[J].Noise and Vibration Control,2015,35(2):175-179.
[10]余梓唐.基于粒子群算法优化支持向量机汽车故障诊断研究[J].计算机应用研究,2012,29(2):572-574.YU Z T.Automotive Fault Diagnosis Based on SVM and Particle Swarm Algorithm[J].Application Research of Computers,2012,29(2):572-574.
[11]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 G J,WANG X L.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.
[12]张淑清,胡永涛,姜安琦,等.基于双树复小波和自适应权重和时间因子的粒子群优化支持向量机的轴承故障诊断[J].中国机械工程,2017,28(3):327-333.ZHANG S Q,HU Y T,JIANG A Q,et al.Bearing Fault Diagnosis Based on DTCWT and AWTFPSO-optimized SVM[J].China Mechanical Engineering,2017,28(3):327-333.
[13]陈法法,汤宝平,董绍江.基于粒子群优化LS-WSVM的旋转机械故障诊断[J].仪器仪表学报,2011,32(12):2747-2753.CHEN F F,TANG B P,DONG S J.Rotating Machinery Fault Diagnosis Based on LS-WSVM with Particle Swarm Optimization[J].Chinese Journal of Scientific Instrument,2011,32(12):2747-2753.
[14]毋文峰,陈小虎,苏勋家.基于经验模式分解的单通道机械信号盲分离[J].机械工程学报,2011,47(4):12-16.WU W F,CHEN X H,SU X J.Blind Source Separation of Signal-channel Mechanical Signal Based on Empirical Mode Decomposition[J].Journal of Mechanical Engineering,2011,47(4):12-16.
[15]HO D,RANDALL R B.Optimization of Bearing Diagnostic Technique Using Simulated and Actual Bearing Fault Signals[J].Mechanical System and Signal Processing,2000,14(5):763-788.