HHT和SVM在机械安全评估与预测中的应用研究
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摘要
为提高大型复杂机械设备运行的安全性和可靠性,在监测机械设备振动状态的基础上,采用希尔伯特-黄变换(HHT)技术处理信号,将获得的振动频域能量值作为机械设备性能退化的特征量;进而采用网格搜索法(GS)和交叉验证法(CV),优化支持向量机模型(SVM)参数,以提高退化特征量预测精度;并据此建立一种状态空间划分法,用以评估并预测机械设备安全状态。最后,用所建立的方法评估并预测无刷直流电机振动状态和相应的安全状态,预测结果的相对误差仅为1.17%。
In this paper,the vibration condition monitoring technique is adopted for mechanical equipment,based on which the HHT method is utilized to processvibration signals; the vibration frequency-domain energy value obtained is taken as the characteristic quantity to represent the performance degradation of mechanical equipment. Then the Grid Search( GS) and Cross Validation( CV) methods are used to optimize the parameters of SVM,so as to improve the prediction accuracy of degradation characteristic quantity. Therefore,a state space division method is developed to assess and predict the safety status of mechanical equipment. Finally,the method developed by the authors was used for assessing and predicting the vibration state and the corresponding safety status of brushless direct current motors. The results show that the prediction error is only 1.17%.
In this paper,the vibration condition monitoring technique is adopted for mechanical equipment,based on which the HHT method is utilized to processvibration signals; the vibration frequency-domain energy value obtained is taken as the characteristic quantity to represent the performance degradation of mechanical equipment. Then the Grid Search( GS) and Cross Validation( CV) methods are used to optimize the parameters of SVM,so as to improve the prediction accuracy of degradation characteristic quantity. Therefore,a state space division method is developed to assess and predict the safety status of mechanical equipment. Finally,the method developed by the authors was used for assessing and predicting the vibration state and the corresponding safety status of brushless direct current motors. The results show that the prediction error is only 1.17%.
引文
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[11]王立,于重重,施彦,等.基于振动信号分析的电机性能退化特征提取[J].计算机仿真,2014,31(4):416-421.WANG Li,YU Chongchong,SHI Yan,et al.Degradation characteristics extraction of electric motor properties based on vibration signal analysis[J].Computer Simulation,2014,31(4):416-421.
[12]JIANG Annan,WANG Shanyong,TANG Shulin.Feedback analysis of tunnel construction using a hybrid arithmetic based on support vector machine and particle swarm optimization[J].Automation in Construction,2011,20(4):482-489.
[13]KONAR P,CHATTOPADHYAY P.Bearing fault detection of induction motor using wavelet and support vector machines(SVMs)[J].Applied Soft Computing,2011,11(6):4 203-4 211.
[14]VANTUNG T,PHAM H T,YANG B S,et al.Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine[J].Mechanical Systems and Signal Processing,2012,32(11):320-330.
[2]ROVERI N,CARCATERRA A.Damage detection in structures under traveling loads by Hilbert-Huang transform[J].Mechanical Systems and Signal Processing,2012,28(4):128-144.
[3]刘俊,王占林,付永领,等.基于改进HHT的一体化电液作动器故障诊断[J].北京航空航天大学学报,2013,39(1):62-66.LIU Jun,WANG Zhanlin,FU Yongling,et al.Fault diagnosis of EHA-VPVM based on improved HHT[J].Journal of Beijing University of Aeronautics and Astronautics,2013,39(1):62-66.
[4]SOUALHIAbdenour,MEDJAHERKKamal,ZERHOUNINNoureddine.Bearing health monitoring based on Hilbert-Huang transform,support vector machine,and regression[J].IEEE Transactions on Instrumentation and Measurement,2015,64(1):52-62.
[5]CARLOS J,CECILIO BV.Support vector machines used to estimate the battery state of charge[J].IEEE Transactions on Power Electronics,2013,28(12):5 919-5 926.
[6]丛林虎,徐廷学,王骞,等.基于退化数据与故障数据的导弹竞争故障预测[J].北京航空航天大学学报,2016,42(3):522-531.CONG Linhu,XU Tingxue,WANG Qian,et al.Missile competing fault prediction based on degradation data and fault data[J].Journal of Beijing University of Aeronautics and Astronautics,2016,42(3):522-531.
[7]BENKEDJOUH T,MEDJAHER K,ZERHOUNI N et al.Health assessment and life prediction of cutting tools based on support vector regression[J].Journal of Intelligent Manufacturing,2015,26(2):213-223.
[8]ZHAO Yan,LIU Enshu,ZHU Junchao et al.Rub-impact fault diagnosis of rotating machinery based on Hilbert-Huang transform[C].IEEE International Conference on Mechatronics and Automation(ICMA).Beijing:IEEE Press,2015:32-36.
[9]田丰,李钫.基于HHT的局部放电在线监测与电缆安全的相关性研究[J].中国安全科学学报,2013,23(7):38-42.TIAN Feng,LI Fang.Study on online detection of local discharge and cable safety based on HHT[J].China Safety Science Journal,2013,23(7):38-42.
[10]董丁稳,李树刚,常心坦,等.基于HHT方法的矿井瓦斯体积分数预测[J].中国安全科学学报,2011,21(9):100-105.DONG Dingwen,LI Shugang,CHANG Xintan,et al.Mine gas concentration prediction based on HHT method[J].China Safety Science Journal,2011,21(9):100-105.
[11]王立,于重重,施彦,等.基于振动信号分析的电机性能退化特征提取[J].计算机仿真,2014,31(4):416-421.WANG Li,YU Chongchong,SHI Yan,et al.Degradation characteristics extraction of electric motor properties based on vibration signal analysis[J].Computer Simulation,2014,31(4):416-421.
[12]JIANG Annan,WANG Shanyong,TANG Shulin.Feedback analysis of tunnel construction using a hybrid arithmetic based on support vector machine and particle swarm optimization[J].Automation in Construction,2011,20(4):482-489.
[13]KONAR P,CHATTOPADHYAY P.Bearing fault detection of induction motor using wavelet and support vector machines(SVMs)[J].Applied Soft Computing,2011,11(6):4 203-4 211.
[14]VANTUNG T,PHAM H T,YANG B S,et al.Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine[J].Mechanical Systems and Signal Processing,2012,32(11):320-330.