基于扭振分析方法的齿轮传动系统故障辨识
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摘要
通过分析齿轮啮合过程的数学模型及典型故障,论证了扭振分析方法在齿轮系统故障诊断上的优越性,并提出一种测量齿轮轴上扭振信息的新方法。在此基础上搭建了齿轮传动系统,通过采用永磁旋转(角)加速度传感器检测齿轮系统各个运行状态下不同轴上的扭振信号;然后,分别对齿轮传动系统轴上的扭振信号和平台的振动信号采用小波包分解,提取各个节点的能量作为特征向量;最后,结合以径向基函数(RBF)为核函数的支持向量机(SVM)分别进行故障的辨识。实验结果表明:轴上的扭振信号在齿轮系统故障诊断上的效果要优于平台振动信号的诊断效果。
A new method to measure the torsional vibration of gear shaft is proposed,though analyzing and studying the mathematical model and typical faults of gear,the superiority of torsional vibration analysis in fault diagnosis of gear system is theoretically demonstrated. To verify the correctness of theoretical analysis,a gear transmission system is set up to detect the torsional vibration signals of different axes under various operating states of the gear system by the permanent magnet rotary( angular) acceleration sensor. Then,the torsional vibration signals of the gear transmission system shaft and the vibration signal of the platform is decomposed by wavelet packet,and the energy of each node is extracted as the eigenvector. Finally,the fault identification is carried out with support vector machine( SVM) with radial basis function( RBF) as kernel function. The experimental result shows that the torsional vibration signal on the shaft is superior to the diagnostic results of the platform vibration signal in fault diagnosis of gear system.
A new method to measure the torsional vibration of gear shaft is proposed,though analyzing and studying the mathematical model and typical faults of gear,the superiority of torsional vibration analysis in fault diagnosis of gear system is theoretically demonstrated. To verify the correctness of theoretical analysis,a gear transmission system is set up to detect the torsional vibration signals of different axes under various operating states of the gear system by the permanent magnet rotary( angular) acceleration sensor. Then,the torsional vibration signals of the gear transmission system shaft and the vibration signal of the platform is decomposed by wavelet packet,and the energy of each node is extracted as the eigenvector. Finally,the fault identification is carried out with support vector machine( SVM) with radial basis function( RBF) as kernel function. The experimental result shows that the torsional vibration signal on the shaft is superior to the diagnostic results of the platform vibration signal in fault diagnosis of gear system.
引文
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[14]何志坚,周志雄,黄向明.基于变分模态分解的关联维数及相关向量机的刀具磨损状态监测[J].计量学报,2018,39(2):182-186.He Z J,Zhou Z X,Huang X M. Tool Wear State Monitoring Based on Variational Mode Decomposition and Correlation Dimension and Relevance Vector Machine[J]. Acta Metrologica Sinica,2018,39(2):182-186.
[15] Wang T Z,Qi J,Xu H,et al. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter[J]. ISA Transactions,2016,60:156-163.
[2]刘彬,蒋金水,赵武,等.轧机转速波动测量的扭振监测实验研究[J].计量学报,2007,28(3):272-275.Liu B,Jiang J S,Zhao W,et al. The Speed Oscillation Measurementof the Torsional Vibration Monitoring of the Rolling Mill[J]. Acta Metrologica Sinica,2007,28(3):272-275.
[3]蒋云帆,廖明夫,王四季,等.航空发动机转子扭振测量新方法[J].振动、测试与诊断,2013,33(3):410-415.Jiang F,Liao M F,Wang S J,et al. New Measuring Method for Torsional Vibration of Aeroengine Rotor[J].Journal of Vibration,Measurement&Diagnosis,2013,33(3):410-415.
[4]王哲.永磁无刷直流电机转矩脉动抑制方法研究[D].哈尔滨:哈尔滨工业大学,2013.
[5]黄震,刘彬.基于多普勒加速度计扭振测量的研究[J].计量学报,2007,28(3):276-279.Huang Z, Liu B. Research on Torsional Vibration Measurement Based on Doppler Accelerometer[J]. Acta Metrologica Sinica,2007,28(3):276-279.
[6]孔祥洪,段发阶,李孟麟,等.光纤束传感器测量轴系扭转振动的研究[J].传感器与微系统,2011,30(1):10-12.Kong X H,Duan F J,Li M L,et al. Research on torsional-axial vibration measurement using fiber-optic bundle sensor[J]. Transducer and Microsystem Technologies,2011,30(1):10-12.
[7] Cerrada M,Zurita G,Cabrera D,et al. Fault diagnosis in spur gears based on genetic algorithm and random forest[J]. Mechanical Systems and Signal Processing.2015,70-71:87-103.
[8]时培明,赵娜,苏冠华,等.变载荷齿轮箱故障信号智能检测方法[J].计量学报,2018,39(6):847-851.Shi P M,Zhao N,Su G H,et al. Intelligent Detection Method of Variable Load Gearbox Fault Signal[J]. Acta Metrologica Sinica,2018,39(6):847-851.
[9]张俊,卞世元,鲁庆,等.准静态工况下渐开线直齿轮齿面磨损建模与分析[J].机械工程学报,2017,53(5):136-145.Zhang J,Dian S Y,Lu Q,et al. Quasi-static-modelbased Wear Analysis of Spur Gears[J]. Journal of Mechanical Engineering,2017,53(5):136-145.
[10]宋立权,赵孝峰,何泽海,等.引入摩擦的周向短弹簧汽车双质量飞轮分析模型及扭振固有特性[J].机械工程学报,2009,45(11):99-105.Song L Q,Zhao X F,He Z H,et al. Analysis Model and Inherent Characteristics of Torsional Vibration of the Dual Mass Flywheel-circumferential Short Spring Introduced Friction[J]. Journal of Mechanical Engineering,2009,45(11):99-105.
[11]冯浩,谭激,张辉,等.一种扭振检测方法及其传感器:201710740261. 0[P]. 2017-11-24.
[12] Liu X F,Bo L,Luo H L,et al. Bearing faults diagnostics based on hybrid LS-SVM and EMD method[J]. Measurement,2015,59:145-166.
[13]孟宗,刘东,马钊,等.基于DEMD局部时频熵和SVM的风电齿轮箱故障诊断方法研究[J].计量学报,2017,38(4):449-452.Meng Z,Liu D,Ma Z,et al. Wind Power Gear Box Fault Diagnosis Based on Differential-based DEMD Local Frequency Entropy and SVM[J]. Acta Metrologica Sinica,2017,38(4):449-452.
[14]何志坚,周志雄,黄向明.基于变分模态分解的关联维数及相关向量机的刀具磨损状态监测[J].计量学报,2018,39(2):182-186.He Z J,Zhou Z X,Huang X M. Tool Wear State Monitoring Based on Variational Mode Decomposition and Correlation Dimension and Relevance Vector Machine[J]. Acta Metrologica Sinica,2018,39(2):182-186.
[15] Wang T Z,Qi J,Xu H,et al. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter[J]. ISA Transactions,2016,60:156-163.