摘要
为解决滚动轴承原始振动信号信噪比低以及带通滤波器参数选择依赖于人的主观经验等影响传统共振解调技术有效应用的问题,提出了EEMD自适应消噪和自适应共振解调相结合的方法。首先利用EEMD自适应地将信号分解成多个分量,通过互相关系数方法进行自适应重构以突出故障特征信号,然后利用谱峭度自动确定带通滤波器的中心频率和带宽,最后对滤波后的信号进行能量算子解调谱分析。数字仿真信号和滚动轴承实验证明了该方法的有效性。
In order to solve problems affecting application of traditional demodulated resonance technology, such as, the lower signal to noise ratio for original signal of rolling bearings and the parameter selection of a band-pass filter depending on experiences of individuals, a method for fault diagnosis of rolling bearings based on combining EEMD adaptive denoising with adaptive demodulated resonance was presented. Firstly, the original signal was decomposed into many components via EEMD adaptively, and adaptive reconstruction was performed by using the correlation coefficient method to highlight fault characteristic signals. Then, the central frequency and bandwidth of a band-pass filter were determined with spectral kurtosis. Last, the filtered signal was analyzed by using energy operator demodulation spectrum. Numerical simulation signals and a rolling bearing test results showed the validity of the proposed method.
出处
《振动与冲击》
EI
CSCD
北大核心
2013年第2期76-80,共5页
Journal of Vibration and Shock
基金
国家自然科学基金重点基金(51035007)
973项目(2011CB706606)
关键词
EEMD自适应消噪
自适应共振解调
谱峭度
故障诊断
EEMD adaptive denoising
adaptive demodulated resonance
spectral kurtosis
fault diagnosis
