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。
Periodic impulse in vibration signals is the key feature to determine the local rolling bearing fault,but under strong noise,the fault feature of rolling bearing is usually very weak and nonstationary. A rolling bearing fault feature extraction method based on wavelet threshold denoising and Teager energy operator is proposed. First of all,the fault signal is pretreated by wavelet threshold denoising method,which can reduce the noise interference and enhance the fault feature. Then using Teager energy operator analysis,the accurate judgment of the fault frequency analysis is realized by FFT（ Fast Fourier Transformation）. Simulation results show that the proposed method can effectively extract the fault characteristics of rolling bearing.
出处
《北京信息科技大学学报（自然科学版）》
2017年第1期40-43,共4页
Journal of Beijing Information Science and Technology University
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)
