摘要
针对滚动轴承早期微弱故障受背景环境噪声影响故障特征难以提取的问题,提出一种基于灰狼算法(GWO)优化最大相关峭度反卷积的滚动轴承振动特征提取与表征方法。该方法采用完全自适应噪声集合经验模态分解(CEEMDAN)将受强背景环境噪声干扰的微弱故障振动信号分解成若干信号分量,并依据峭度指标和相关系数作为筛选指标对各信号分量进行筛选和重构,通过GWO优化的最大相关峭度反卷积(MCKD)滤除重构信号中的噪声成分同时增强微弱故障特征成分,并对其进行包络解调实现微弱故障特征的提取。基于滚动轴承实验台数据及真实涡扇发动机整机数据开展了滚动轴承故障特征提取与表征方法有效性的综合验证。结果表明:该方法可有效滤除滚动轴承微弱故障振动信号中的强背景环境噪声成分同时增强微弱故障特征,经主轴承外圈微弱故障实验数据验证可知去噪信号与原始信号的峰值因子相比提高了2.43,有效增强振动信号中的冲击性成分,实现滚动轴承微弱故障特征的有效提取与表征。
In view of the problem that the weak fault characteristics of rolling bearings in the early stage affected by background environmental noise are difficult to be extracted,a rolling bearing vibration feature extraction and characterization method was proposed based on maximum correlated kurtosis deconvolution optimized by gray wolf optimization(GWO)algorithm.The complete ensemble empirical model decomposition with adaptive noise(CEEMDAN)was used to decompose the weak fault vibration signal disturbed by strong background environmental noise into several signal components,and the signal components were screened and reconstructed according to the kurtosis and correlation coefficient as the screening index in this method.The maximum correlated kurtosis deconvolution(MCKD)optimized by the GWO algorithm filtered out the noise components in the reconstructed signal,enhanced the weak fault feature components and performed envelope demodulation to extract the weak fault features.A comprehensive verification of the effectiveness of the vibration signal fault feature extraction and characterization method was carried out based on the rolling bearing test bench data and the real whole machine data of the aero-engine.The results showed that this method can effectively filter out the strong background environmental noise part in the weak fault vibration signal and enhance the weak fault characteristics,indicating that the peak factor of the denoising signal processed by this method increased by 2.43 compared with the original vibration signal in the turbofan engine experiment,so it effectively enhanced the shock component in the vibration signal.The method proposed can be used as one of the effective methods for fault diagnosis of aero-engine.
作者
李彦徵
栾孝驰
杨杰
沙云东
郭小鹏
徐石
LI Yanzheng;LUAN Xiaochi;YANG Jie;SHA Yundong;GUO Xiaopeng;XU Shi(Liaoning Key Lab of Advanced Test Technology for Aerospace Propulsion System,School of Aero-engine,Shenyang Aerospace University,Shenyang 110136,China;Shenyang Engine Research Institute,Aero Engine Corporation of China,Shenyang 110015,China)
出处
《航空动力学报》
北大核心
2025年第3期354-368,共15页
Journal of Aerospace Power
基金
重点基础研究项目
辽宁省教育厅面上项目(JYTMS20230249)
大学生创新创业训练计划项目(202310143012)
中国航发产学研合作项目(HFZL2018CXY017)。
关键词
涡扇发动机
滚动轴承
故障特征提取
灰狼算法
最大相关峭度反卷积
turbofan engine
rolling bearing
fault feature extraction
gray wolf optimization algorithm
maximum correlated kurtosis deconvolution
