期刊文献+

变转速下基于广义解调算法的滚动轴承故障诊断 被引量:7

Rolling element bearing fault diagnosis based on generalized demodulation algorithm under variable rotational speed
下载PDF
导出
摘要 变转速条件下故障轴承的冲击间隔会相应的发生改变,导致以包络分析为代表以恒转速为前提的故障诊断方法失效。阶比分析因其在消除频谱模糊方面的有效性,成为处理变转速故障轴承信号最为常规的方法。然而,上述方法在对信号重采样的过程中存在幅值误差、包络畸变以及计算效率低等问题。为此,从滚动轴承的振动特性出发,提出了无需角域重采样的基于广义解调算法的滚动轴承故障诊断方法。整个算法主要包括五部分:(1)利用快速谱峭度算法确定最优带通滤波参数,并对原始振动信号进行滤波;(2)根据转速脉冲信号计算并拟合转速曲线;(3)通过转频方程以及滚动轴承的故障特征系数确定广义解调算法所需要的相位函数;(4)根据相位函数对滤波信号进行广义解调,对解调信号进行快速傅里叶变换(Fast Fourier Transform,FFT)获取解调信号的频谱图;(5)观察频谱图中的峰值,更改故障特征系数重复步骤(3)-(4),最终确定轴承故障类型。仿真及实测的故障轴承信号分析证明了新算法对变转速下滚动轴承故障诊断的有效性。 The intervals of the faulty rolling element bearing impulses will not appear periodically under variable rotational speeds,which lead to the envelope analysis method, which is based on the assumption of constant speed, no longer applicable. The order tracking is the most familiar algorithm to process time-varying speed bearing signals because of its effectiveness in e-liminating the fuzzy spectrum. However, it also has defects such as amplitude error and envelope deformation. Hence,based on the characteristics of rolling element bearing, a method of rolling element bearing fault diagnosis is proposed by the general-ized demodulation method without angular domain resampling. The method consists of six main steps: (a) signal filtering via optimal bandpass filter parameters obtained by fast kurtosis algorithm; ( b) to obtain the speed curve based on the rotational speed pulse signal; (c) to calculate the phase-function-based speed curve and fault characteristic coefficient; (d ) to apply the generalized demodulation algorithm to the filter signal and obtain the frequency spectrum via fast Fourier transform(FFT) ; (e) to search the peak from the spectrum and then modify the fault characteristic coefficient and repeat steps(c)-(d) to identify the fault type of bearing. The studies of simulated and experimental faulty bearing signals indicate that the proposed method is ef-fective to diagnose faulty bearing under time-varying rotational speed.
出处 《振动工程学报》 EI CSCD 北大核心 2017年第5期865-873,共9页 Journal of Vibration Engineering
基金 中央高校基本科研业务费专项资金资助项目(M17JB00270)
关键词 故障诊断 滚动轴承 变转速 瞬时故障特征频率 广义解调算法 fault diagnosis rolling element bearing time-varying speed instantaneous fault characteristic frequency general-ized demodulation algorithm
  • 相关文献

参考文献4

二级参考文献67

  • 1丁康,孔正国.振动调幅调频信号的调制边频带分析及其解调方法[J].振动与冲击,2005,24(6):9-12. 被引量:27
  • 2程军圣,于德介,杨宇.基于支持矢量回归机的Hilbert-Huang变换端点效应问题的处理方法[J].机械工程学报,2006,42(4):23-31. 被引量:75
  • 3GUO D,PENG Z K.Vibration analysis of a cracked rotor using Hilbert-Huang transform[J].Mechanical Systems and Signal Processing,2007,21(8):3 030-3 041.
  • 4CHOW T W S,SHI Hai.Induction machine fault diagnstic analysis with wavelet technique[J].IEEE Transactions on Industrial Electronics,2004,51(3):558-565.
  • 5HUANG N E,SHEN Z,LONG S R.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proc.R.Soc.Lond.A,1998(454):903-995.
  • 6CHENG Junsheng,YU Dejie,TANG Jiashi,et al.Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis[J].Mechanism and Machine Theory,2008,43(6):712-723.
  • 7QIN S R,ZHONG Y M.A new algorithm of HilbertHuang transform[J].Mechanical Systems and Signal Processing,2006,20(8):1 941-1 952.
  • 8LEI Yaguo,HE Zhengjia,ZI Yanyang.Application of the EEMD method to rotor fault diagnosis of rotating machinery[J].Mechanical Systems and Signal Processing,2009,23(4):1 327-1 338.
  • 9WU Fangji,QU Liangsheng.An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery[J].Journal of Sound and Vibration,2008,314 (3-5):586-602.
  • 10OLHEDE S,WALDEN A T.A generalized demodulation approach to time-frequency projections for multicom-ponent signals[J].Proceedings of the Royal Society A,2005,461(2059):2 159-2 179.

共引文献34

同被引文献74

引证文献7

二级引证文献40

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部