基于参数优化MLOG与SAM的滚动轴承早期故障诊断

Early Fault Diagnosis of Rolling Bearings Based on Parameter Optimization MLOG and SAM

针对强背景噪声环境下轴承早期故障特征不易提取问题,提出麻雀搜索算法(SSA)优化改进拉普拉斯高斯滤波器(MLOG)结合频谱调幅(SAM)的早期故障诊断方法。以滤波后信号的峭度最大值为指标,用SSA算法自适应寻找滤波器阶数和标准差的最优参数;用MLOG滤波器对故障信号滤波,去除部分噪声;用SAM方法对滤波后信号的幅值赋予不同的权重进行重构,计算重构信号的平方包络谱,提取故障特征频率。通过仿真信号和不同试验台的真实数据验证,同时对比PSO和GA优化MLOG结果,表明所提SSA-MLOG-SAM方法对滚动轴承早期故障特征提取的有效性和可行性。

To solve the problem that bearing early fault features are difficult to be extracted under strong background noise, an early fault diagnosis method based on the sparrow search algorithm(SSA) and the modified Laplacian of Gaussian(MOLG) filter combined with spectral amplitude modulation(SAM) was proposed.The maximum kurtosis of the filtered signal was taken as the index, and the optimal parameters of filter order and standard deviation were found adaptively by using SSA.The fault signal was filtered by using MLOG filter to remove noise.The SAM method was used to assign different weights to the amplitude of the filtered signal for reconstruction, the square envelope spectrum of the reconstructed signal was calculated, and the fault characteristic frequency was extracted.The simulation signals and real data from different test stations were verified.The comparison between PSO and GA optimized MLOG results show that the proposed SSA-MLOG-SAM method is effective and feasible for early fault feature extraction of rolling bearings.