摘要
针对传统算法难以准确提取强背景噪声下航空发动机转子系统微弱故障特征的问题,提出了变分非线性调频模态分解(VNCMD)结合Birge-Massart阈值降噪的航空发动机转子故障诊断方法。首先利用VNCMD对转子故障信号进行分解,根据峭度值及相关系数准则筛选有效信号分量,然后采用Birge-Massart阈值降噪方法对该信号分量进行降噪处理,最后对降噪后的信号进行包络解调,提取出转子故障特征信息。并通过对比经验模态分解(EMD)结合Birge-Massart阈值降噪的方法的实验结果,结果表明:该方法能够有效提升转子系统故障信息提取能力,实现转子系统故障更有效的诊断。
Aiming at the problem that traditional algorithm is difficult to accurately extract the weak fault characteristics of aeroengine rotor system under strong background noise,a fault diagnosis method based on variational nonlinear chirp mode decomposition(VNCMD)combined with Birge-Massart threshold noise reduction is proposed.Firstly,vncmd is used to decompose the rotor fault signal,and the effective signal component is selected according to kurtosis value criterion and correlation coefficient,Then the signal component is denoised by Birge-Massart threshold denoising method.Finally,the envelope demodulation is used to extract the rotor fault feature iiifbrmation.By comparing the experimental results of empirical mode decomposition(EMD)combined with Birge-Massart threshold denoising method,the results show that this method can effectively improve the extraction ability of rotor systemfault information and achieve more effective diagnosis of rotor systemfault.
作者
梁春辉
刘晓波
LIANG Chun-hui;LIU Xiao-bo(School of Aeronautical Manufacturing Engineering,Nanchang Hangkong University,Jiangxi Nanchang 330063,China)
出处
《机械设计与制造》
北大核心
2023年第4期201-205,共5页
Machinery Design & Manufacture
基金
国家自然科学基金(51365040)
南昌航空大学研究生创新专项资金资助项目(YC2019010)。
