基于阶次跟踪的变速齿轮啮合频率振动评估方法
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摘要
针对变速齿轮在某一转速范围内运行时产生共振导致强烈振动的问题,有效地对变速齿轮啮合频率进行评估是减少和规避此类故障的关键。但由于变速齿轮振动信号在时域呈现非平稳性,传统的齿轮信号分析方法无法有效地提取出特征频率,并且齿轮箱振动源较多且存在背景噪声,导致阶次谱成分复杂。有鉴于此,将阶次跟踪和经验模态分解相结合,提出一种基于阶次跟踪的变速齿轮啮合频率振动评估方法。首先采用阶次跟踪方法对某转速下齿轮的时域信号进行等角度重采样;然后对阶次域信号进行经验模态分解,提取出包含齿轮啮合信息的本征模函数(Intrinsic Mode Function,IMF)分量;再利用阶次谱分析提取出齿轮啮合阶次所对应的幅值;最后通过比较不同转速下的齿轮啮合阶次的幅值大小,对齿轮的振动变化进行评估。在实际现场齿轮信号的应用结果表明:该方法得到的阶次谱中啮合阶次明显,并且能够有效地实现变速齿轮的共振转速识别。
In view of the problem of strong vibration caused by resonance of gear when it operates within a range of speed,it is the key to reduce or even avoid this kind of fault by evaluating the meshing frequency of gear gear effectively. However,because of the non-stationary of the vibration signal in the time domain,the traditional method of analysing signal can not extract the feature frequency effectively. In addition,due to many vibration sources in the gearbox,the order spectrum is complex. In view of this,it proposes a vibration evaluation method for gear meshing frequency based on order tracking and Empirical Mode Decomposition( EMD). First,the order tracking method is used to resampling the time domain signal of the gear at a varying speed condition.After the empirical mode decomposition of the angular signals is carried out to extract the Intrinsic Mode Function( IMF) component containing the gear meshing information,the order spectrum analysis is used to extract the amplitude of the gear meshing order. Finally,the magnitude of gear meshing order is compared and evaluated for different speeds. The application results of the actual field gear signal show that the mesh order is obvious in the order spectrum obtained by the proposed method and can effectively realize the resonance speed recognition.
In view of the problem of strong vibration caused by resonance of gear when it operates within a range of speed,it is the key to reduce or even avoid this kind of fault by evaluating the meshing frequency of gear gear effectively. However,because of the non-stationary of the vibration signal in the time domain,the traditional method of analysing signal can not extract the feature frequency effectively. In addition,due to many vibration sources in the gearbox,the order spectrum is complex. In view of this,it proposes a vibration evaluation method for gear meshing frequency based on order tracking and Empirical Mode Decomposition( EMD). First,the order tracking method is used to resampling the time domain signal of the gear at a varying speed condition.After the empirical mode decomposition of the angular signals is carried out to extract the Intrinsic Mode Function( IMF) component containing the gear meshing information,the order spectrum analysis is used to extract the amplitude of the gear meshing order. Finally,the magnitude of gear meshing order is compared and evaluated for different speeds. The application results of the actual field gear signal show that the mesh order is obvious in the order spectrum obtained by the proposed method and can effectively realize the resonance speed recognition.
引文
[1]朱圆.基于信号处理的齿轮箱故障诊断方法研究[D].成都:电子科技大学,2017.
[2]刘鸣洲.微弱机械冲击信号的检测与提取方法研究[D].杭州:浙江大学,2018.
[3] MAHESWARI R V,UMAMAHESWARI R. Trends in nonstationary signal processing techniques applied to vibration analysis of wind turbine drive train———A contemporary survey[J]. Mechanical Systems and Signal Processing,2017(85):296-311.
[4] FYFE K R,MUNCK E D S. Analysis of computed order tracking[J]. Mechanical Systems and Signal Processing,1997,11(2):187-205.
[5] HEA Guolin,KANG Dinga,LIA Weihua. A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique[J]. Renewable Energy,2016,87(1):364-375.
[6]葛杏卫,邹新光,崔彦平.基于阶次分析的变工况齿轮信号采集系统设计[J].设计与研究,2016(7):57-61.
[7]崔砚宏,叶文龙,黄林.阶次跟踪在齿轮箱噪声源识别上的应用[J].机械传动,2012,36(7):111-116.
[8] HUANG N E,SHEN Z,LONG S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London A Mathematical Physical and Engineering Series,1998,454(971):903-995.
[9]吕跃刚,陈盼娣,李雪田.基于EMD和Hilbert包络解调的滚动轴承故障诊断方法研究[J].煤矿机械,2013,34(11):280-282.
[10] VOLD H,LEURIDAN H. High resolution order tracking at extreme slew rates,using Kalman tracking filters[J]. Shock&Vibration,1995,2(6):507-515.
[11] PRTTER R W. Tracking and resampling method and apparatus for monitoring the performance of rotating machines:4912661[P]. 1990-3-27.
[12] BAI M,HUANG J,HONG M. Fault diagnosis of rotating machinery using an intelligent order tracking system[J]. Journal of Sound&Vibration,2005,280(3/4/5):699-718.
[2]刘鸣洲.微弱机械冲击信号的检测与提取方法研究[D].杭州:浙江大学,2018.
[3] MAHESWARI R V,UMAMAHESWARI R. Trends in nonstationary signal processing techniques applied to vibration analysis of wind turbine drive train———A contemporary survey[J]. Mechanical Systems and Signal Processing,2017(85):296-311.
[4] FYFE K R,MUNCK E D S. Analysis of computed order tracking[J]. Mechanical Systems and Signal Processing,1997,11(2):187-205.
[5] HEA Guolin,KANG Dinga,LIA Weihua. A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique[J]. Renewable Energy,2016,87(1):364-375.
[6]葛杏卫,邹新光,崔彦平.基于阶次分析的变工况齿轮信号采集系统设计[J].设计与研究,2016(7):57-61.
[7]崔砚宏,叶文龙,黄林.阶次跟踪在齿轮箱噪声源识别上的应用[J].机械传动,2012,36(7):111-116.
[8] HUANG N E,SHEN Z,LONG S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London A Mathematical Physical and Engineering Series,1998,454(971):903-995.
[9]吕跃刚,陈盼娣,李雪田.基于EMD和Hilbert包络解调的滚动轴承故障诊断方法研究[J].煤矿机械,2013,34(11):280-282.
[10] VOLD H,LEURIDAN H. High resolution order tracking at extreme slew rates,using Kalman tracking filters[J]. Shock&Vibration,1995,2(6):507-515.
[11] PRTTER R W. Tracking and resampling method and apparatus for monitoring the performance of rotating machines:4912661[P]. 1990-3-27.
[12] BAI M,HUANG J,HONG M. Fault diagnosis of rotating machinery using an intelligent order tracking system[J]. Journal of Sound&Vibration,2005,280(3/4/5):699-718.