摘要
提出了一种基于多普勒调制时移Laplace小波的列车轴承故障声信号瞬态成分快速提取方法,包含“先粗后精”两个步骤:1)瞬态参数粗估计,利用现有的多普勒调制等周期Laplace小波模型粗略估计瞬态参数;2)参数精确估计与瞬态成分提取,构造多普勒调制时移Laplace小波模型,使用逐个匹配的策略进行瞬态参数精确估计和瞬态成分的提取。所提方法具有以下优点:1)更高的精度,使用的多普勒调制时移Laplace小波模型在时域内仅有一个时延参数定位的小波成分,能够解决周期瞬态模型在提取伪周期瞬态成分时匹配误差问题;2)高效率,由于使用了周期瞬态模型粗略估计瞬态成分参数,因此在瞬态成分逐个提取的过程中小波参数的范围可以设的很小,实验对比分析结果显示,与直接提取方式相比效率提高了71.46%。本研究提供了一种从含有多普勒畸变的列车轴承故障声信号中精确地、高效率地提取瞬态成分的方法。
A fast transient component extraction method of the train bearing fault acoustic signal is proposed,which is based on Doppler modulated time-shifting Laplace wavelet.It includes two steps that are rough estimation first and precise identification.The first is rough estimation of transient parameters.The existing periodic Doppler modulated Laplace wavelet model is used to roughly estimate the transient parameters.The second is precise parameter estimation and transient component extraction.A Doppler modulated time-shifting Laplace wavelet model is formulated,which uses one-by-one matching strategy to accurately estimate the transient parameters and extract the transient components.The proposed method has two advantages,which are high accuracy and high efficiency.For high accuracy,the Doppler modulation time-shifted Laplace wavelet model has only one wavelet component for positioning the delay parameter in the time domain,which can solve the matching error problem caused by the pseudo-period of the transient component.For high efficiency,because the periodic transient model is used to roughly estimate the parameters of the transient components,the range of the wavelet parameters can be set very small in the process of extracting the transient components one by one.The experiment comparison and analysis results show that the efficiency is increased by 71.46%,compared with the direct extraction method.This study provides a method to accurately and efficiently extract transient components from train bearing fault acoustic signals containing Doppler distortion.
作者
刘方
翟涛涛
侯超强
滕繁荣
刘永斌
Liu Fang;Zhai Taotao;Hou Chaoqiang;Teng Fanrong;Liu Yongbin(School of Electrical Engineering and Automation,Anhui University,Hefei 230601,China;National Engineering Laboratory of Energy-Saving Motor&Control Technology,Anhui University,Hefei 230601,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2022年第3期40-48,共9页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(51875001,52075001)项目资助。
