BFA优化掩膜参数的轴承故障诊断方法的研究

Research on Bearing Fault Diagnosis Method Based on BFA Optimized Mask Parameters

掩膜信号法一定程度上削弱了信号分解结果中的模态混叠现象,其分解结果主要受到掩膜信号的幅值和掩膜频率的影响。为获得参数最优解,摒弃了传统计算获得的方法,提出了细菌觅食算法优化掩膜参数的滚动轴承故障诊断方法。首先利用参数寻优的BFA算法优化掩膜信号的幅值和掩膜频率,得到最优参数组合,利用参数优化后的掩膜信号处理故障信号得到频谱更加纯净的本征模函数,最终根据相关系数准则选取最佳分量进行频谱分析。实验结果表明优化参数后掩膜信号处理下的故障信号抗模态混叠能力更强,分量融合后故障特征更明显。

The mask signal method weakens the modal aliasing in the signal decomposition result to a certain extent,and its decomposition result is mainly affected by the amplitude of the mask signal and the mask frequency. In order to obtain the parameter optimal solution,the traditional calculation method is abandoned,and a rolling bearing fault diagnosis method for optimizing the mask parameters of the bacterial foraging algorithm is proposed. First,use the parameter-optimized BFA algorithm to optimize the mask signal amplitude and mask frequency to obtain the optimal parameter combination. Use the parameter optimized mask signal to process the fault signal to obtain a more pure eigenmode function. The coefficient criterion selects the best component for spectrum analysis. The experimental results show that the fault signal under mask signal processing has better anti-modal aliasing ability after optimizing the parameters,and the fault characteristics are more obvious after component fusion.