摘要
针对滚动轴承早期故障微弱特征难以提取的问题,提出了一种基于Hermitian小波时间-能量谱的滚动轴承故障诊断方法.该方法针对轴承故障振动信号具有奇异性的特点,首先利用Hermitian小波对原始信号进行连续小波变换;再根据小波变换的结果求取信号能量在时间轴上的分布情况,利用谱峭度指标作为选择最佳累积尺度的标准,得到时间-小波能量分布;最后对时间-小波能量分布进行谱分析得到时间-小波能量谱以提取故障特征.利用时间-小波能量谱对仿真信号和轴承外圈及内圈点蚀故障信号进行分析.结果表明:该方法可有效地提取出强噪声环境下微弱故障的特征成分,并与普通的时间-小波能量谱作对比,特征提取效果更为明显,非常适用于滚动轴承早期故障诊断.
Aiming at the difficulty of weak fault feature extraction for rolling bearing early fault, a fault diagnosis method based on Hermitian time-wavelet energy spectrum was proposed for rolling bearing fault diagnosis. First, the Hermitian wavelet to original signal was applied to acquire the continues wavelet transformation. Then the signal energy distribution along the time axis was calculated according to the wavelet transform results. The spectrum kurtosis was used as the selection criterion of optimal cumulation scale to obtain the time-wavelet energy distribution. Finally, the spectrum of time-energy distribution was calculated to obtain the time-energy spectrum and extract the fault feature. The results of the simulation signal and vibration signals of outer and inner rings pitting fault show that this method can effectively extract weak fault feature in strong noise background, and is superior compared with ordinary timewavelet energy spectrum; therefore, it is suitable for early fault feature extraction of bearings.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2014年第3期328-334,共7页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(51075009)
北京市优秀人才培养资助计划项目(2011D005015000006)
