滚动轴承复合故障的混合协同诊断方法
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摘要
针对传统复合故障诊断方法存在故障难以完全分离的缺点,提出了滚动轴承复合故障的混合协同诊断方法。首先对观测信号的协方差矩阵进行奇异值分解,求出白化矩阵并对复合故障信号进行白化处理;然后,利用联合对角化方法对白化后的故障矩阵进行对角化变形,通过最小化对角化程度函数得到正交矩阵;最后,通过正交矩阵估计故障源信号矩阵,实现复合故障的分离;由于二阶盲辨识方法分离出的故障信号间存在无序性以及相似性,导致分离信号故障类型难以确定,因此将分离后的故障信号进行短时傅里叶变换,通过分离信号的时频谱图与原信号时频谱图进行比较,并根据趋势一致性确认所对应的故障类型。最终,以广东省石化装备故障诊断重点实验室的轴承数据进行实验论证,结果表明,二阶盲辨识协同短时傅里叶变换能有效将滚动轴承的复合故障信号分离出来,工程上具备可操作性和极大的应用价值。
Traditional composite faults diagnosis approaches applied in isolating rolling bearing mixed faults are not effectively. To overcome this defect, this paper proposes a hybrid collaborative diagnosis method for rolling bearing composite faults. Firstly, by using second order blind identification method, we calculate the whiten matrix of fault signals. Then, by minimizing the degree of diagonalization function, we can realize joint diagonalization on whiten fault matrix. Finally, we use singular value decomposition for the orthogonal matrix, further we can estimate the signal matrix of fault sources from the compound fault signals. But through the above procedure, we can only get the out-of-order signals. If there are some similar signals, it is hard to recognize them by our intuitions. So we next apply the short-term Fourier transform to solve this problem, by comparing the time-frequency spectrograms trends between isolated and original signals, we can clearly identify the fault types. At last, to validate its effectiveness, we put the fault data from the Key Laboratory of Fault Diagnosis of Petrochemical Equipment in Guangdong Province into this proposed method, the results show its practical value in mechanical engineering.
Traditional composite faults diagnosis approaches applied in isolating rolling bearing mixed faults are not effectively. To overcome this defect, this paper proposes a hybrid collaborative diagnosis method for rolling bearing composite faults. Firstly, by using second order blind identification method, we calculate the whiten matrix of fault signals. Then, by minimizing the degree of diagonalization function, we can realize joint diagonalization on whiten fault matrix. Finally, we use singular value decomposition for the orthogonal matrix, further we can estimate the signal matrix of fault sources from the compound fault signals. But through the above procedure, we can only get the out-of-order signals. If there are some similar signals, it is hard to recognize them by our intuitions. So we next apply the short-term Fourier transform to solve this problem, by comparing the time-frequency spectrograms trends between isolated and original signals, we can clearly identify the fault types. At last, to validate its effectiveness, we put the fault data from the Key Laboratory of Fault Diagnosis of Petrochemical Equipment in Guangdong Province into this proposed method, the results show its practical value in mechanical engineering.
引文
[1]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):55-59,64.SUI Wen-tao,ZHANG Dan,WANG W.Fault diagnosis of rolling element bearing based on EMD and MKD[J].Journal of Vibration and Shock,2015,34(9):55-59,64.
[2]许昕,潘宏侠,潘铭志.独立分量分析在自动机振动信号处理中的应用[J].振动、测试与诊断,2016,36(1):120-125,202.XU Xin,PAN Hong-xia,PAN Ming-zhi.Application of independent component analysis in vibration signal processing of automata[J].Journal of Vibration,Measurement&Diagnosis,2016,36(1):120-125,202.
[3]黄文涛,董振振,孔繁朝.引入撞击力的滚动轴承内圈故障振动模型[J].振动与冲击,2016,35(17):121-126.HUANG Wen-tao,DONG Zhen-zhen,KONG Fan-chao.Vibration model of rolling element bearings with inner race faults considering impact force[J].Journal of Vibration and Shock,2016,35(17):121-126.
[4]YAN C.Fault diagnostic expert system of rolling element bearing based on CLIPS[J].Journal of Information&Computational Science,2013,10(10):3053-3062.
[5]万书亭,詹长庚,豆龙江.滚动轴承故障特征提取的EMD-频谱自相关方法[J].振动、测试与诊断,2016,36(6):1161-1167,1240.WAN Shu-ting,ZHAN Chang-geng,DOU Long-jiang.The EMD-spectrum auto correlation method of rolling bearing fault feature extraction[J].Journal of Vibration,Measurement&Diagnosis,2016,36(6):1161-1167,1240.
[6]凌启辉,闫晓强,张义方.基于S变换的热连轧机耦合振动特征提取[J].振动、测试与诊断,2016,36(1):115-119,201-202.LING Qi-hui,YAN Xiao-qiang,ZHANG Yi-fang.Based on the S transform feature extraction of coupled vibration in hot strip mill[J].Journal of Vibration,Measurement&Diagnosis,2016,36(1):115-119,201-202.
[7]HUANG Da-rong,TANG Jian-ping,ZHAO Ling.A fault diagnosis method of power systems based gray system theory[J].Mathematical Problems in Engineering,2014(3):1-11.
[8]ZHAO Ling,HUANG Da-rong,QIN Yi.Fault diagnosis for gearbox based on improved empirical mode decomposition[J].Shock and Vibration,2015(6):1-9.
[9]赵玲,刘小峰,娄路.复小波分解联合SVD提取振动信号非平稳特征[J].振动、测试与诊断,2015,35(4):672-676.ZHAO Ling,LIU Xiao-feng,LOU Lu.Complex wavelet decomposition combined with SVD to extract non-stationary features of vibration signals[J].Journal of Vibration,Measurement&Diagnosis,2015,35(4):672-676.
[10]马新娜,杨绍普.滚动轴承复合故障诊断的自适应方法研究[J].振动与冲击,2016,35(10):145-150.MA Xin-na,YANG Shao-pu.Adaptive compound fault diagnosis of rolling bearings[J].Journal of Vibration and Shock,2016,35(10):145-150.
[11]唐宏宾,吴运新,滑广军,等.基于EMD包络谱分析的液压泵故障诊断方法[J].振动与冲击,2012,31(9):44-48.TANG Hong-bin,WU Yun-xin,HUA Guang-jun,et al.Fault diagnosis of pump using EMD and envelope spectrum analysis[J].Journal of Vibration and Shock,2012,31(9):44-48.
[12]钟先友,赵春华,陈保家,等.基于MCKD和重分配小波尺度谱的旋转机械复合故障诊断研究[J].振动与冲击,2015,34(7):156-161.ZHONG Xian-you,ZHAO Chun-hua,CHEN Bao-jia,et al.Rotating machinery fault diagnosis based on maximum correlation kurtosis deconvolution and reassigned wavelet scalogram[J].Journal of Vibration and Shock,2015,34(7):156-161.
[13]赵玲,秦树人,李宁,等.运用改进掩膜信号法的经验模态分解[J].振动、测试与诊断,2010,30(4):434-439.ZHAO Ling,QIN Shu-ren,LI Ning,et al.Empirical mode decomposition using improved mask signal method[J].Journal of Vibration,Measurement&Diagnosis,2010,30(4):434-439.
[14]赵玲,刘小峰,秦树人,等.HHT新方法及其在齿轮箱故障诊断中的应用[J].振动、测试与诊断,2011,31(2):207-211.ZHAO Ling,LIU Xiao-feng,QIN Shu-ren,et al.New HHT method and its application in gearbox fault diagnosis[J].Journal of Vibration,Measurement&Diagnosis,2011,31(2):207-211.
[15]张可,周东华,柴毅.复合故障诊断技术综述[J].控制理论与应用,2015,32(9):1144-1157.ZHANG Ke,ZHOU Dong-hua,CHAI Yi.Review of multiple fault diagnosis methods[J].Control Theory&Applications,2015,32(9):1144-1157.
[16]黄大荣,陈长沙,孙国玺,等.复杂装备轴承多重故障的线性判别分析与反向传播神经网络的协作故障诊断方法[J].兵工学报,2017,38(8):1651-1657.HUANG Da-rong,CHEN Chang-sha,SUN Guo-xi,et al.Linear discriminant analysis and back propagation neural network cooperative diagnosis method for multiple faults of complex equipment bearings[J].ACTA Armamentar,2017,38(8):1651-1657.
[2]许昕,潘宏侠,潘铭志.独立分量分析在自动机振动信号处理中的应用[J].振动、测试与诊断,2016,36(1):120-125,202.XU Xin,PAN Hong-xia,PAN Ming-zhi.Application of independent component analysis in vibration signal processing of automata[J].Journal of Vibration,Measurement&Diagnosis,2016,36(1):120-125,202.
[3]黄文涛,董振振,孔繁朝.引入撞击力的滚动轴承内圈故障振动模型[J].振动与冲击,2016,35(17):121-126.HUANG Wen-tao,DONG Zhen-zhen,KONG Fan-chao.Vibration model of rolling element bearings with inner race faults considering impact force[J].Journal of Vibration and Shock,2016,35(17):121-126.
[4]YAN C.Fault diagnostic expert system of rolling element bearing based on CLIPS[J].Journal of Information&Computational Science,2013,10(10):3053-3062.
[5]万书亭,詹长庚,豆龙江.滚动轴承故障特征提取的EMD-频谱自相关方法[J].振动、测试与诊断,2016,36(6):1161-1167,1240.WAN Shu-ting,ZHAN Chang-geng,DOU Long-jiang.The EMD-spectrum auto correlation method of rolling bearing fault feature extraction[J].Journal of Vibration,Measurement&Diagnosis,2016,36(6):1161-1167,1240.
[6]凌启辉,闫晓强,张义方.基于S变换的热连轧机耦合振动特征提取[J].振动、测试与诊断,2016,36(1):115-119,201-202.LING Qi-hui,YAN Xiao-qiang,ZHANG Yi-fang.Based on the S transform feature extraction of coupled vibration in hot strip mill[J].Journal of Vibration,Measurement&Diagnosis,2016,36(1):115-119,201-202.
[7]HUANG Da-rong,TANG Jian-ping,ZHAO Ling.A fault diagnosis method of power systems based gray system theory[J].Mathematical Problems in Engineering,2014(3):1-11.
[8]ZHAO Ling,HUANG Da-rong,QIN Yi.Fault diagnosis for gearbox based on improved empirical mode decomposition[J].Shock and Vibration,2015(6):1-9.
[9]赵玲,刘小峰,娄路.复小波分解联合SVD提取振动信号非平稳特征[J].振动、测试与诊断,2015,35(4):672-676.ZHAO Ling,LIU Xiao-feng,LOU Lu.Complex wavelet decomposition combined with SVD to extract non-stationary features of vibration signals[J].Journal of Vibration,Measurement&Diagnosis,2015,35(4):672-676.
[10]马新娜,杨绍普.滚动轴承复合故障诊断的自适应方法研究[J].振动与冲击,2016,35(10):145-150.MA Xin-na,YANG Shao-pu.Adaptive compound fault diagnosis of rolling bearings[J].Journal of Vibration and Shock,2016,35(10):145-150.
[11]唐宏宾,吴运新,滑广军,等.基于EMD包络谱分析的液压泵故障诊断方法[J].振动与冲击,2012,31(9):44-48.TANG Hong-bin,WU Yun-xin,HUA Guang-jun,et al.Fault diagnosis of pump using EMD and envelope spectrum analysis[J].Journal of Vibration and Shock,2012,31(9):44-48.
[12]钟先友,赵春华,陈保家,等.基于MCKD和重分配小波尺度谱的旋转机械复合故障诊断研究[J].振动与冲击,2015,34(7):156-161.ZHONG Xian-you,ZHAO Chun-hua,CHEN Bao-jia,et al.Rotating machinery fault diagnosis based on maximum correlation kurtosis deconvolution and reassigned wavelet scalogram[J].Journal of Vibration and Shock,2015,34(7):156-161.
[13]赵玲,秦树人,李宁,等.运用改进掩膜信号法的经验模态分解[J].振动、测试与诊断,2010,30(4):434-439.ZHAO Ling,QIN Shu-ren,LI Ning,et al.Empirical mode decomposition using improved mask signal method[J].Journal of Vibration,Measurement&Diagnosis,2010,30(4):434-439.
[14]赵玲,刘小峰,秦树人,等.HHT新方法及其在齿轮箱故障诊断中的应用[J].振动、测试与诊断,2011,31(2):207-211.ZHAO Ling,LIU Xiao-feng,QIN Shu-ren,et al.New HHT method and its application in gearbox fault diagnosis[J].Journal of Vibration,Measurement&Diagnosis,2011,31(2):207-211.
[15]张可,周东华,柴毅.复合故障诊断技术综述[J].控制理论与应用,2015,32(9):1144-1157.ZHANG Ke,ZHOU Dong-hua,CHAI Yi.Review of multiple fault diagnosis methods[J].Control Theory&Applications,2015,32(9):1144-1157.
[16]黄大荣,陈长沙,孙国玺,等.复杂装备轴承多重故障的线性判别分析与反向传播神经网络的协作故障诊断方法[J].兵工学报,2017,38(8):1651-1657.HUANG Da-rong,CHEN Chang-sha,SUN Guo-xi,et al.Linear discriminant analysis and back propagation neural network cooperative diagnosis method for multiple faults of complex equipment bearings[J].ACTA Armamentar,2017,38(8):1651-1657.