小波阈值去噪和能量算子的滚动轴承故障诊断

发布时间：2018-01-09 10:23

本文关键词：小波阈值去噪和能量算子的滚动轴承故障诊断　出处：《北京信息科技大学学报(自然科学版)》2017年01期 　论文类型：期刊论文

【摘要】：振动信号中的周期性冲击现象是判断滚动轴承局部故障的关键特征,但是在强噪声背景下滚动轴承故障特征通常表现为非平稳信号且非常微弱。提出了基于小波阈值去噪和Teager能量算子的滚动轴承故障特征提取方法。首先用小波阈值降噪法对故障信号进行预处理,减少噪声干扰,增强故障特征,然后利用Teager能量算子分析处理,最后通过FFT进行频域分析实现故障类型的准确判断。滚动轴承故障的仿真信号分析结果表明,该方法能够有效提取滚动轴承故障特征。
[Abstract]:The periodic shock phenomenon in the vibration signal is the key feature to judge the local fault of the rolling bearing. However, the fault feature of rolling bearing is usually non-stationary and very weak under strong noise background. A method of fault feature extraction for rolling bearing based on wavelet threshold denoising and Teager energy operator is proposed. First, the wavelet threshold denoising method is used to preprocess the fault signal. The noise interference is reduced and the fault feature is enhanced. Then the Teager energy operator is used to analyze and deal with the problem. Finally, the fault type is accurately judged by frequency domain analysis by FFT. The simulation signal analysis of rolling bearing fault shows that the method can effectively extract the fault characteristics of rolling bearing.
【作者单位】：北京信息科技大学现代测控技术教育部重点实验室;
【基金】：国家自然科学基金资助项目(51575055) 国家重大专项资助项目(2015ZX04001002)
【分类号】：TH133.33
【正文快照】： 0引言滚动轴承是机械系统中的关键部件,研究滚动轴承的状态监测和故障诊断技术,对于保证机械设备的安全,稳定运行具有重要意义。滚动轴承故障诊断的关键是故障特征提取,轴承发生故障时,其振动信号通常呈非平稳状态。常规的滚动轴承信号检测方法有取样积分方法、时域的相关方法

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