+高级检索
基于初值优化的自适应最稀疏时频分析方法
DOI:
作者:
作者单位:

作者简介:

通讯作者:

基金项目:


Adaptive and Sparsest Time-frequency Analysis MethodBased on Initial Value Optimization
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    自适应最稀疏时频分析(adaptive and sparsest time-frequency analysis,ASTFA)是一种新的时频分析方法,该方法需要事先确定较为准确的初始值,缺乏自适应性.针对ASTFA存在的问题,提出了基于初值优化的ASTFA方法.该方法使用残余量的能量作为优化目标函数,使用不同的初始值对信号进行分解,当残余量的能量最小时,则认为该初始值为最优初始值.因此,该方法能够自适应地寻找最优的初始值,增加了ASTFA方法的自适应性.采用仿真信号将该方法与原ASTFA方法进行对比,结果表明该方法能自适应地得到更准确的分解结果.对仿真信号和滚动轴承故障数据进行分析,结果表明ASTFA在抑制端点效应和模态混淆、抗噪声性能、提高分量的准确性等方面要优于经验模态分解(empirical mode decomposition,EMD),并能有效应用于滚动轴承故障诊断.

    Abstract:

    Adaptive and sparsest time-frequency analysis(ASTFA)is a new method for time-frequency analysis.ASTFA is lack of adaptivity as comparatively accurate initial values have to be set beforehand.Aiming to solve the problem existed in ASTFA,adaptive and sparsest time-frequency analysis method based on initial value optimization was proposed.The energy value of the residue is applied as the optimization objective function,and different initial values are used for signal decomposition.Initial values are considered to be the best only if the energy value of the corresponding residue is the smallest.Therefore,the adaptivity of ASTFA method is improved by the proposed method as the best initial values can be found adaptively. Simulation signal is applied to compare the proposed method and the initial ASTFA method.The results show that more accurate decomposition results can be adaptively obtained by using the proposed method.Analysis of simulation signal and rolling bearing fault signal shows that compared with empirical mode decomposition(EMD)method,the proposed method is superior at least in restraining end effect and mode mixing,anti-noise performance and gaining more accurate components.Meanwhile,the proposed method is effective in rolling bearing fault diagnosis.

    参考文献
    相似文献
    引证文献
文章指标
  • PDF下载次数:
  • HTML阅读次数:
  • 摘要点击次数:
  • 引用次数:
引用本文

彭延峰,刘贞涛,程军圣, 杨宇,刘燕飞.基于初值优化的自适应最稀疏时频分析方法[J].湖南大学学报:自然科学版,2017,44(8):50~56

复制
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2017-09-20
  • 出版日期:
作者稿件一经被我刊录用,如无特别声明,即视作同意授予我刊论文整体的全部复制传播的权利,包括但不限于复制权、发行权、信息网络传播权、广播权、表演权、翻译权、汇编权、改编权等著作使用权转让给我刊,我刊有权根据工作需要,允许合作的数据库、新媒体平台及其他数字平台进行数字传播和国际传播等。特此声明。
关闭