基于小波—包络的重载货运列车滚动轴承振动故障诊断
发布时间：2018-09-14 09:17
【摘要】：滚动轴承作为重载货运列车的重要部件,其工作状态直接影响到货车列车的提速和安全运行。滚动轴承发生故障时,一般为磨损故障和损伤类故障。磨损故障是由于轴承磨损造成零件间隙逐渐变大,振动加强。这是一类渐变性的故障,振动波形变化缺乏规律,具有较强的随机性,但通频带的振动幅值变化量往往能清晰地反映磨损的严重程度。损伤类故障是指当轴承元件滚过表面损伤点时,就会产生一个突变的冲击脉冲力,引起轴承和机械设备的共振,这是损伤类故障的基本特征。磨损类故障一般是一个比较长的发展过程,可以采取定期的轴承振动总量监测,进行趋势分析作状态预报。而损伤类故障是一种具有较强突发性而又比较危险,早期故障症状又比较难识别的一类故障,并且是进行故障诊断时重点研究方向。事实上,滚动轴承产生故障时,由于轴承元件受到刚度非线性、接触摩擦、零件间隙和外载荷等的影响,其振动信号往往会表现为非平稳特征,因此,如何从非平稳振动信号中提取滚动轴承故障特征信息,是滚动轴承的故障诊断中的重中之重。 针对滚动轴承故障振动信号的非平稳特性,结合小波变换、Hilbert变换、包络谱细化分析的优点,提出先将压电式加速度传感器采集的轴承振动信号进行db10小波滤波重构,对滤波重构信号进行Hilbert变换得到包络信号,最后对包络谱细化得到振动幅值谱图,根据细化后的幅值谱图判别轴承故障的小波-包络分析方法,并通过仿真和实验分析滚动轴承的故障,验证理论分析的正确性。所提出的基于小波-包络分析的重载货运列车滚动轴承振动故障诊断方法同传统的时频分析、小波变换、共振解调方法相比有所突破,诊断的速度和准确度都有所提高,对国内重载货运列车滚动轴承故障诊断的发展有一定的意义。 经过在滚动轴承振动实验台采集的实验轴承的振动信号数据的分析和处理,能从具有强干扰和噪声的轴承振动加速度信号中提取故障特征,实现了滚动轴承故障诊断。
[Abstract]:As an important part of heavy-haul freight train, rolling bearing has a direct impact on the speed and safety of the train. Rolling bearing failure, generally for wear and damage type of fault. Wear failure is caused by bearing wear caused by the gradual increase in clearance, vibration strengthening. This is a kind of gradually changing fault. The variation of vibration waveform is lack of regularity and has strong randomness. However, the variation of vibration amplitude in passband can clearly reflect the serious degree of wear. The damage type fault refers to when the bearing element rolls over the surface damage point, will produce a sudden impact pulse force, causes the bearing and the mechanical equipment resonance, this is the damage class fault basic characteristic. Generally, the wear type fault is a relatively long developing process, which can be monitored regularly by the total vibration of bearing, and the trend analysis can be used to forecast the state of the bearing. The damage type fault is a kind of fault which has strong sudden and dangerous, and the early fault symptom is difficult to identify, and it is the research direction of fault diagnosis. As a matter of fact, when rolling bearings fail, the vibration signals are often non-stationary because the bearing elements are affected by stiffness nonlinearity, contact friction, part clearance and external load, etc. How to extract fault feature information of rolling bearing from non-stationary vibration signal is the most important in fault diagnosis of rolling bearing. Aiming at the non-stationary characteristic of rolling bearing fault vibration signal, combining the advantages of wavelet transform Hilbert transform and envelope spectrum thinning analysis, the db10 wavelet filter reconstruction of bearing vibration signal collected by piezoelectric acceleration sensor is put forward. The envelope signal is obtained by the Hilbert transform of the filtered reconstructed signal. Finally, the amplitude spectrum of vibration is obtained by thinning the envelope spectrum, and the wavelet envelope analysis method is used to distinguish the fault of bearing according to the refined amplitude spectrum. The fault of rolling bearing is analyzed by simulation and experiment to verify the correctness of theoretical analysis. Compared with the traditional time-frequency analysis, wavelet transform and resonance demodulation, the method of vibration fault diagnosis of rolling bearing of heavy-haul freight train based on wavelet envelope analysis has a breakthrough, and the speed and accuracy of diagnosis are improved. It has certain significance for the development of rolling bearing fault diagnosis of heavy haul freight train in China. Through the analysis and processing of the vibration signal data collected from the rolling bearing vibration test rig, the fault characteristics of the bearing vibration acceleration signal with strong interference and noise can be extracted, and the fault diagnosis of the rolling bearing can be realized.
【学位授予单位】：中南大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH165.3
本文编号：2242266
[Abstract]:As an important part of heavy-haul freight train, rolling bearing has a direct impact on the speed and safety of the train. Rolling bearing failure, generally for wear and damage type of fault. Wear failure is caused by bearing wear caused by the gradual increase in clearance, vibration strengthening. This is a kind of gradually changing fault. The variation of vibration waveform is lack of regularity and has strong randomness. However, the variation of vibration amplitude in passband can clearly reflect the serious degree of wear. The damage type fault refers to when the bearing element rolls over the surface damage point, will produce a sudden impact pulse force, causes the bearing and the mechanical equipment resonance, this is the damage class fault basic characteristic. Generally, the wear type fault is a relatively long developing process, which can be monitored regularly by the total vibration of bearing, and the trend analysis can be used to forecast the state of the bearing. The damage type fault is a kind of fault which has strong sudden and dangerous, and the early fault symptom is difficult to identify, and it is the research direction of fault diagnosis. As a matter of fact, when rolling bearings fail, the vibration signals are often non-stationary because the bearing elements are affected by stiffness nonlinearity, contact friction, part clearance and external load, etc. How to extract fault feature information of rolling bearing from non-stationary vibration signal is the most important in fault diagnosis of rolling bearing. Aiming at the non-stationary characteristic of rolling bearing fault vibration signal, combining the advantages of wavelet transform Hilbert transform and envelope spectrum thinning analysis, the db10 wavelet filter reconstruction of bearing vibration signal collected by piezoelectric acceleration sensor is put forward. The envelope signal is obtained by the Hilbert transform of the filtered reconstructed signal. Finally, the amplitude spectrum of vibration is obtained by thinning the envelope spectrum, and the wavelet envelope analysis method is used to distinguish the fault of bearing according to the refined amplitude spectrum. The fault of rolling bearing is analyzed by simulation and experiment to verify the correctness of theoretical analysis. Compared with the traditional time-frequency analysis, wavelet transform and resonance demodulation, the method of vibration fault diagnosis of rolling bearing of heavy-haul freight train based on wavelet envelope analysis has a breakthrough, and the speed and accuracy of diagnosis are improved. It has certain significance for the development of rolling bearing fault diagnosis of heavy haul freight train in China. Through the analysis and processing of the vibration signal data collected from the rolling bearing vibration test rig, the fault characteristics of the bearing vibration acceleration signal with strong interference and noise can be extracted, and the fault diagnosis of the rolling bearing can be realized.
【学位授予单位】：中南大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH165.3
【引证文献】
相关硕士学位论文 前2条
1 陈京;便携式设备故障监测诊断系统关键技术研究[D];北京化工大学;2012年
2 孙鹏冲;基于谐波小波和加速度包络的城轨列车轴承故障诊断研究[D];北京交通大学;2012年
，本文编号：2242266
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