基于改进Hilbert分析的陀螺电机轴承保持架故障特征识别
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摘要
针对陀螺电机轴承保持架故障信号振动频率小、量级低,常被强噪声淹没而难以识别的问题,采用自适应噪声完备集合经验模态分解改进HHT算法,并与自动提取敏感IMF方法相结合以提取保持架故障特征,进行故障状态识别。通过对某挠性支承陀螺仪中存在保持架故障轴承的实例分析表明,该算法可准确提取出多阶保持架故障特征频率。
The fault signal of bearing cages in gyro motor is often submerged by strong noise and difficult to identify due to small vibration frequency and low order of magnitude. The complete ensemble empirical mode decomposition with adaptive noise is used to improve HHT algorithm,and it is combined with automatic extraction of sensitive IMF method to extract fault feature of cages and identify fault status. The analysis of bearings with fault cages in flexible supporting gyroscope shows that the algorithm is able to extract accurately multi-order fault feature frequency of cages.
The fault signal of bearing cages in gyro motor is often submerged by strong noise and difficult to identify due to small vibration frequency and low order of magnitude. The complete ensemble empirical mode decomposition with adaptive noise is used to improve HHT algorithm,and it is combined with automatic extraction of sensitive IMF method to extract fault feature of cages and identify fault status. The analysis of bearings with fault cages in flexible supporting gyroscope shows that the algorithm is able to extract accurately multi-order fault feature frequency of cages.
引文
[1]董磊,周灏,潘龙飞,等.由定子电流信号分析陀螺电机滚珠轴承故障诊断与分类[J].中国惯性技术学报,2015(3):415-420.
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[9]邓四二,顾金芳,崔永存,等.高速圆柱滚子轴承保持架动力学特性分析[J].航空动力学报,2014,29(1):207-215.
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[11]CHEN J S,YU D J,YANG Y.A fault diagnosis approach for roller bearing based on EMD method and ARmodel[J].Mechanical Systems and Signal Processing,2006,20(2):350-362.
[12]祝洪宇,胡静涛,高雷,等.基于变频器供电侧电流Hilbert解调制方法的空载电机转子断条故障诊断[J].仪器仪表学报,2014,35(1):140-147.
[13]夏均忠,刘远宏,李树珉,等.应用Hilbert变换和ZFFT提取变速器齿轮故障特征[J].振动与冲击,2013,32(6):63-66.
[14]潘海宁,张军,秦明,等.基于能量谱特征的变速风机振动调制信号的检测方法[J].中国电机工程学报,2014,34(增刊1):166-171.
[15]雷亚国.基于改进Hilbert-Huang变换的机械故障诊断[J].机械工程学报,2011,47(5):71-77.
[16]杨宇,于德介,程军圣,等.基于EMD与神经网络的滚动轴承故障诊断方法[J].振动与冲击,2005,24(1):85-88.
[17]向丹,葛爽.基于EMD样本熵-LLTSA的故障特征提取方法[J].航空动力学报,2014,29(7):1535-1542.
[18]楼军伟,胡赤兵,赵家黎.EEMD样本熵在轴承故障SVM识别中的研究[J].机械传动,2014,38(3):41-44.
[19]吴小涛,杨锰,袁晓辉,等.基于峭度准则EEMD及改进形态滤波方法的轴承故障诊断[J].振动与冲击,2015,34(2):38-44.
[20]黄锦殿,柴卫东.基于小波分析的氢涡轮泵低温轴承保持架故障特征辨识[J].火箭推进,2011,37(2):43-47.
[2]梁存良,王德伟,巩孟祥,等.航空发动机主轴承保持架断裂故障分析[J].轴承,2016(5):24-26.
[3]PANG B T,LI J,LIU H,et a1.A simulation study on optimal oil spraying mode for high-speed rolling bearing[J].Journal of Achievements in Materials and Manufacturing Engineering,2008,31(2):553-557.
[4]翟强,闫柯,张优云,等.高速角接触球轴承腔内气相流动与传热特性研究[J].西安交通大学学报,2014,48(12):29-33,40.
[5]姚廷强,王立华,刘孝保,等.变工况下角接触球轴承保持架稳定性分析[J].振动与冲击,2016,35(18):172-180.
[6]张建忠,马国翰.滚动轴承保持架动力学研究进展[J].轴承,2011(1):56-60.
[7]SHAH D S,PATEL V N.A review of dynamic modeling and fault identification methods for rolling element bearing[J].Procedia Technology,2014,14:447-456.
[8]姚廷强,黄亚宇,王立华.圆柱滚子轴承多体接触动力学研究[J].振动与冲击,2015(7):15-23,32.
[9]邓四二,顾金芳,崔永存,等.高速圆柱滚子轴承保持架动力学特性分析[J].航空动力学报,2014,29(1):207-215.
[10]LIU B,RIEMENSCHNEIDER S,XU Y.Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum[J].Mechanical Systems and Signal Processing,2006,20(3):718-734.
[11]CHEN J S,YU D J,YANG Y.A fault diagnosis approach for roller bearing based on EMD method and ARmodel[J].Mechanical Systems and Signal Processing,2006,20(2):350-362.
[12]祝洪宇,胡静涛,高雷,等.基于变频器供电侧电流Hilbert解调制方法的空载电机转子断条故障诊断[J].仪器仪表学报,2014,35(1):140-147.
[13]夏均忠,刘远宏,李树珉,等.应用Hilbert变换和ZFFT提取变速器齿轮故障特征[J].振动与冲击,2013,32(6):63-66.
[14]潘海宁,张军,秦明,等.基于能量谱特征的变速风机振动调制信号的检测方法[J].中国电机工程学报,2014,34(增刊1):166-171.
[15]雷亚国.基于改进Hilbert-Huang变换的机械故障诊断[J].机械工程学报,2011,47(5):71-77.
[16]杨宇,于德介,程军圣,等.基于EMD与神经网络的滚动轴承故障诊断方法[J].振动与冲击,2005,24(1):85-88.
[17]向丹,葛爽.基于EMD样本熵-LLTSA的故障特征提取方法[J].航空动力学报,2014,29(7):1535-1542.
[18]楼军伟,胡赤兵,赵家黎.EEMD样本熵在轴承故障SVM识别中的研究[J].机械传动,2014,38(3):41-44.
[19]吴小涛,杨锰,袁晓辉,等.基于峭度准则EEMD及改进形态滤波方法的轴承故障诊断[J].振动与冲击,2015,34(2):38-44.
[20]黄锦殿,柴卫东.基于小波分析的氢涡轮泵低温轴承保持架故障特征辨识[J].火箭推进,2011,37(2):43-47.