基于经验小波变换和谱负熵的飞机轴承故障信号分析  

Analysis of bearing fault signal based on empirical wavelet transform and spectral negative entropy

作　　者：叶柯华 马鹏宇 李印欣 YE Kehua;MA Pengyu;LI Yinxin(Shanghai Aircraft Design&Research Institute,Shanghai 201210,China)

机构地区：[1]上海飞机设计研究院,上海201210

出　　处：《民用飞机设计与研究》2024年第3期11-22,共12页Civil Aircraft Design & Research

摘　　要：轴承是飞机动力系统和机械结构等重要的组成部分,其复杂的故障特征对飞行安全具有重大影响。为提高轴承故障诊断准确率,提出一种基于经验小波变换和谱负熵的轴承故障信号分析方法。该方法首先针对故障信号进行经验小波变换,后计算各分量时域谱负熵和频域谱负熵。针对平均谱负熵无法自适应调整冲击性和周期性权重系数的问题,提出一种自适应平均谱负熵。而后,以峭度最大化为适应度函数,利用灰狼优化算法对构成分量个数和自适应平均谱负熵比例系数进行优化,实现对故障信号重构。分析结果表明:1)重构信号较好地保留了故障冲击特征成分;2)故障信号重构后,峭度大幅提高,信噪比明显改善;3)时域谱负熵和峭度皆可量化信号中冲击成分,但峭度受噪声影响更为显著。The bearing is an important part of aircraft power system and mechanical structure,which has complex fault characteristics and has the crucial impact on flight safety.In order to improve the accuracy of bearing fault diagnosis,a bearing fault signal analysis method based on empirical wavelet transform and spectral negative entropy is proposed in this paper.The method firstly calculated the time-domain spectral negative entropy and frequency-domain spectral negative entropy of each component after decomposing the fault signal by empirical wavelet transform.In view of the fact that the average spectral negative entropy cannot adjust the impact and periodic weight coefficients adaptively,an adaptive average spectral negative entropy is proposed.Then using the kurtosis maximization as fitness function,grey wolf optimization algorithm is used to optimize the number of components and the adaptive average spectral negative entropy proportional coefficient to reconstruct the fault signal.The results show that:1)the reconstructed signal retains fault impact components of fault characteristics;2)after the fault signal reconstruction,the kurtosis and signal-to-noise ratio are significantly improved;3)both time-domain spectral negative entropy and kurtosis can quantify the impact components of the signal,however kurtosis is more sensitively affected by noise.

关 键 词：轴承 经验小波变换 谱负熵 灰狼优化算法 信号分析 

分 类 号：V222[航空宇航科学与技术—飞行器设计]