变转速下基于广义解调算法的滚动轴承故障诊断
发布时间：2019-01-27 18:10
【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。
[Abstract]:Under the condition of variable speed, the impact interval of the fault bearing will change accordingly, which leads to the failure of the fault diagnosis method, which is represented by the envelope analysis and takes the constant speed as the premise. Because of its effectiveness in eliminating spectrum ambiguity, order analysis has become the most common method to deal with variable speed fault bearing signals. However, there are some problems such as amplitude error, envelope distortion and low computational efficiency in the process of signal resampling. Therefore, based on the vibration characteristics of rolling bearings, a fault diagnosis method based on generalized demodulation algorithm is proposed. The whole algorithm consists of five parts: (1) using fast spectral kurtosis algorithm to determine the optimal band-pass filtering parameters and filtering the original vibration signal; (2) calculating and fitting the rotational speed curve according to the rotational speed pulse signal; (3) the phase function needed by the generalized demodulation algorithm is determined by the rotation frequency equation and the fault characteristic coefficient of the rolling bearing. (4) based on the phase function, the filtered signal is generalized demodulated and the demodulated signal is obtained by (Fast Fourier Transform,FFT (Fast Fourier transform). (5) observing the peak value in the spectrum diagram, changing the fault characteristic coefficient and repeating steps (3)-(4) to determine the bearing fault type. Simulation and actual analysis of fault bearing signal show that the new algorithm is effective for rolling bearing fault diagnosis under variable speed.
【作者单位】： 北京交通大学机械与电子控制工程学院;北京交通大学载运工具先进制造与测控技术教育部重点实验室;
【基金】：中央高校基本科研业务费专项资金资助项目(M17JB00270)
【分类号】：TH133.31
本文编号：2416548
[Abstract]:Under the condition of variable speed, the impact interval of the fault bearing will change accordingly, which leads to the failure of the fault diagnosis method, which is represented by the envelope analysis and takes the constant speed as the premise. Because of its effectiveness in eliminating spectrum ambiguity, order analysis has become the most common method to deal with variable speed fault bearing signals. However, there are some problems such as amplitude error, envelope distortion and low computational efficiency in the process of signal resampling. Therefore, based on the vibration characteristics of rolling bearings, a fault diagnosis method based on generalized demodulation algorithm is proposed. The whole algorithm consists of five parts: (1) using fast spectral kurtosis algorithm to determine the optimal band-pass filtering parameters and filtering the original vibration signal; (2) calculating and fitting the rotational speed curve according to the rotational speed pulse signal; (3) the phase function needed by the generalized demodulation algorithm is determined by the rotation frequency equation and the fault characteristic coefficient of the rolling bearing. (4) based on the phase function, the filtered signal is generalized demodulated and the demodulated signal is obtained by (Fast Fourier Transform,FFT (Fast Fourier transform). (5) observing the peak value in the spectrum diagram, changing the fault characteristic coefficient and repeating steps (3)-(4) to determine the bearing fault type. Simulation and actual analysis of fault bearing signal show that the new algorithm is effective for rolling bearing fault diagnosis under variable speed.
【作者单位】： 北京交通大学机械与电子控制工程学院;北京交通大学载运工具先进制造与测控技术教育部重点实验室;
【基金】：中央高校基本科研业务费专项资金资助项目(M17JB00270)
【分类号】：TH133.31
【相似文献】
中国博士学位论文全文数据库 前2条
1 李永波;滚动轴承故障特征提取与早期诊断方法研究[D];哈尔滨工业大学;2017年
2 王晓龙;基于振动信号处理的滚动轴承故障诊断方法研究[D];华北电力大学(北京);2017年
中国硕士学位论文全文数据库 前2条
1 马艳丽;基于全矢MEMD的滚动轴承状态退化研究[D];郑州大学;2017年
2 金兵;基于信息融合与VPMCD的滚动轴承智能诊断研究[D];郑州大学;2017年
，本文编号：2416548
本文链接：https://www.wllwen.com/jixiegongchenglunwen/2416548.html
