基于频域振幅灰色关联度的转子故障模式辨识
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摘要
动压滑动轴承支撑的旋转机械经常出现包含多种故障状态的复合故障,针对传统的辨识方法很难一次判断出多种故障状态的问题,提出了一种对频域振幅进行灰色关联分析的旋转机械转子故障模式辨识新方法。在对动压滑动轴承支撑下的实验转子振动故障信号进行滤波后,以故障信号特征频率的幅值作为特征向量,进行归一化处理,用处理过的数据作为故障模式特征向量构建标准故障模式的特征向量判别矩阵。通过计算得到待识别样本信号归一化幅值与标准故障特征向量判别矩阵中各元素之间的频域振幅灰色关联度数值,并与选定的基准判别系数进行对比完成故障模式辨识。实例表明,在不同转速频率下该方法均能取得良好的效果。
The actual failures of mechanical systems include a variety of fault conditions,which are complex problems.In view of that the traditional identification methods are difficult to determine a variety of fault conditions,a new identification method is proposed based on gray relational analysis of failure modes about rotating machinery.First the data from sliding bearings of double cross-rotor test bed is filtered and then the fault characteristic frequency of the signal amplitude is extracted as a feature vector,characteristic frequency amplitude of different speeds are normalized,which is used as the failure mode eigenvectors to determine the elements of the matrix and build the discriminant matrix.Finally,base failure mode identification can be got by comparing the calculated normalized signal amplitude with the standard fault feature vector matrix elements and by determining the amplitude of gray correlation coefficient.Those examples show that the method can achieve good results.
The actual failures of mechanical systems include a variety of fault conditions,which are complex problems.In view of that the traditional identification methods are difficult to determine a variety of fault conditions,a new identification method is proposed based on gray relational analysis of failure modes about rotating machinery.First the data from sliding bearings of double cross-rotor test bed is filtered and then the fault characteristic frequency of the signal amplitude is extracted as a feature vector,characteristic frequency amplitude of different speeds are normalized,which is used as the failure mode eigenvectors to determine the elements of the matrix and build the discriminant matrix.Finally,base failure mode identification can be got by comparing the calculated normalized signal amplitude with the standard fault feature vector matrix elements and by determining the amplitude of gray correlation coefficient.Those examples show that the method can achieve good results.
引文
[1]钟秉林,黄仁.机械故障诊断学[M].北京:机械工业出版社,2003:6.
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[3]万书亭,佟海侠,董炳辉.基于最小二乘支持向量机的滚动轴承故障诊断[J].振动、测试与诊断,2010,30(2):149-152,209.Wan Shuting,Tong Haixia,Dong Binghui.Bearing fault diagnosis using wavelet packet transform and least square support vector machines[J].Journal of Vibration,Measurement&Diagnosis,2010,30(2):149-152,209.(in Chinese)
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[9]马辉,赵鑫,赵群超,等.时频分析在旋转机械故障诊断中的应用[J].振动与冲击,2007,26(3):61-63,158.Ma Hui,Zhao Xin,Zhao Qunchao,et al.Application of time-frequency analysis to rotating machinery fault diagnosis[J].Journal of Vibration and Shock,2007,26(3):61-63,158.(in Chinese)
[10]刘义,王国玉,柯宏发.一种基于灰色距离测度的小样本数据区间估计方法[J],系统工程与电子技术,2008,30(1):116-119.Liu Yi,Wang Guoyu,Ke Hongfa.Approach of parameter estimation for small samples based on grey distance measure[J].Systems Engineering and Electronics,2008,30(1):116-119.(in Chinese)
[11]Ke Hongfa,Chen Yongguang,Liu Yi.Data processing of small samples based on grey distance information approach[J].Journal of Systems Engineering and Electronics,2007,18(2):281-289.
[12]施红星,刘思峰,方志耕,等.灰色周期关联度模型及其应用研究[J],中国管理科学,2008,16(3):131-136.Shi Hongxing,Liu Sifeng,Fang Zhigeng,et al.The model of grey periodic incidence and their rehabilitation[J].Chinese Journal of Management Science,2008,16(3):131-136.(in Chinese)
[13]窦唯,刘占生,王政先.旋转机械振动故障诊断的灰度-梯度共生矩阵方法[J].航空动力学报,2008,23(10):1939-1943.Dou Wei,Liu Zhansheng,Wang Zhengxian.Vibration fault diagnosis method based on gray level-gradient cooccurrence matrix for rotating machinery[J].Journal of Aerospace Power,2008,23(10):1939-1943.(in Chinese)
[14]李天梅,邱静,刘冠军,等.基于模糊灰色关联分析的故障样本集评估方法[J].仪器仪表学报,2008,29(10):2196-2200.Li Tianmei,Qiu Jing,Liu Guanjun,et al.Evaluation method for failure sample set based on fuzzy grey relation analysis[J].Chinese Journal of Scientific Instrument,2008,29(10):2196-2200.(in Chinese)
[2]霍天龙,赵荣珍,胡宝权.基于熵带法与PSO优化的SVM转子故障诊断[J].振动、测试与诊断,2011,31(3):279-284,392.Huo Tianlong,Zhao Rongzhen,Hu Baoquan.Fault diagnosis for rotor systems based on entropy band method and support vector machine optimized by PSO[J].Journal of Vibration,Measurement&Diagnosis,2011,31(3):279-284,392.(in Chinese)
[3]万书亭,佟海侠,董炳辉.基于最小二乘支持向量机的滚动轴承故障诊断[J].振动、测试与诊断,2010,30(2):149-152,209.Wan Shuting,Tong Haixia,Dong Binghui.Bearing fault diagnosis using wavelet packet transform and least square support vector machines[J].Journal of Vibration,Measurement&Diagnosis,2010,30(2):149-152,209.(in Chinese)
[4]Jack L B,Nandi A K.Fault detection using support vector machines and artificial neural networks,augmented by genetic algorithms[J].Mechanical Systems and Signal Processing,2002,16(2-3):373-390.
[5]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[M].北京:科学出版社,2010:17-29.
[6]施红星,刘思峰,方志耕,等.灰色振幅关联度模型[J].系统工程理论与实践,2010,30(10):1828-1833.Shi Hongxing,Liu Sifeng,Fang Zhigeng,et al.Grey amplitude incidence model[J].Systems EngineeringTheory&Practice,2010,30(10):1828-1833.(in Chinese)
[7]沈路,周晓军,张文斌.基于形态滤波与灰色关联度的滚动轴承故障诊断[J].振动与冲击,2009,28(11):17-20.Shen Lu,Zhou Xiaojun,Zhang Wenbin.Fault diagnosis of rolling element bearing based on morphological filter and grey incidence[J].Journal of Vibration and Shock,2009,28(11):17-20.(in Chinese)
[8]罗跃纲,闻帮椿.双跨松动转子-轴承系统周期运动稳定性[J].振动与冲击,2007,26(8):9-12.Luo Yuegang,Wen Bangchun.Stability of two-span rotor-bearing system with pedestal looseness fault[J].Journal of Vibration and Shock,2007,26(8):9-12.(in Chinese)
[9]马辉,赵鑫,赵群超,等.时频分析在旋转机械故障诊断中的应用[J].振动与冲击,2007,26(3):61-63,158.Ma Hui,Zhao Xin,Zhao Qunchao,et al.Application of time-frequency analysis to rotating machinery fault diagnosis[J].Journal of Vibration and Shock,2007,26(3):61-63,158.(in Chinese)
[10]刘义,王国玉,柯宏发.一种基于灰色距离测度的小样本数据区间估计方法[J],系统工程与电子技术,2008,30(1):116-119.Liu Yi,Wang Guoyu,Ke Hongfa.Approach of parameter estimation for small samples based on grey distance measure[J].Systems Engineering and Electronics,2008,30(1):116-119.(in Chinese)
[11]Ke Hongfa,Chen Yongguang,Liu Yi.Data processing of small samples based on grey distance information approach[J].Journal of Systems Engineering and Electronics,2007,18(2):281-289.
[12]施红星,刘思峰,方志耕,等.灰色周期关联度模型及其应用研究[J],中国管理科学,2008,16(3):131-136.Shi Hongxing,Liu Sifeng,Fang Zhigeng,et al.The model of grey periodic incidence and their rehabilitation[J].Chinese Journal of Management Science,2008,16(3):131-136.(in Chinese)
[13]窦唯,刘占生,王政先.旋转机械振动故障诊断的灰度-梯度共生矩阵方法[J].航空动力学报,2008,23(10):1939-1943.Dou Wei,Liu Zhansheng,Wang Zhengxian.Vibration fault diagnosis method based on gray level-gradient cooccurrence matrix for rotating machinery[J].Journal of Aerospace Power,2008,23(10):1939-1943.(in Chinese)
[14]李天梅,邱静,刘冠军,等.基于模糊灰色关联分析的故障样本集评估方法[J].仪器仪表学报,2008,29(10):2196-2200.Li Tianmei,Qiu Jing,Liu Guanjun,et al.Evaluation method for failure sample set based on fuzzy grey relation analysis[J].Chinese Journal of Scientific Instrument,2008,29(10):2196-2200.(in Chinese)