摘要
针对低信噪比(SNR),复杂噪声工况下,复合故障信号特征难以提取的问题。提出基于相空间重构融入最大相关雷尼熵解卷积的信号特征提取方法,该方法以雷尼熵为敏感特征范数,以最大相关雷尼熵解卷积为基本方法,并在其中融入具有噪声抑制特性和分解特性的相空间重构技术。结果表明:雷尼熵与峭度相比,在故障灵敏度相当并略好的情况下,对偶发噪声敏感度仅为峭度的18.4%。通过仿真验证,实验数据验证以及台架实验验证,证明了本文方法与现有的对比方法相比,在提取复合故障信号特征方面具有优势。
In order to solve the problem of complex fault signal feature extraction under the condition of low signal-to-noise ratio(SNR) and complex noise,a feature extraction method based on phase space reconstruction and maximum correlation Rényi entropy deconvolution was proposed.Rényi entropy was taken as the performance index,and the maximum correlation Rényi entropy deconvolution was taken as the basic method,and the phase space reconstruction technique was incorporated with the characteristics of noise suppression and decomposition.Results showed that the sensitivity of Raney entropy was only 18.4% of the kurtosis when the fault sensitivity was equal to and slightly better than that of kurtosis.Through simulation,experimental data and bench test,this method was proved superior to existing comparison methods in extracting the features of composite fault signals.
作者
张震
刘保国
周万春
冯伟
ZHANG Zhen;LIU Baoguo;ZHOU Wanchun;FENG Wei(Henan Key Laboratory for Superabrasive Grinding Equipment,Henan University of Technology,Zhengzhou 450001,China;School of Mechanical,Electrical and Vehicle Engineering,Zhengzhou University of Technology,Zhengzhou 450044,China)
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2023年第4期889-900,共12页
Journal of Aerospace Power
基金
国家自然科学基金(12072106)
河南省超硬磨料磨削装备重点实验室开放课题(JDKFJJ2022008)
郑州工程技术学技术研发推广与转化基金(zjz202209)。
关键词
雷尼熵
相空间重构
复合故障
滚动轴承
解卷积
Rényi entropy
phase space reconstruction
composite fault
rolling bearing
deconvolution
