噪声与振动控制2019，Vol.39Issue(5)：185-190,6.DOI:10.3969/j.issn.1006-1355.2019.05.035
基于蛾火优化的自适应最稀疏时频分析方法及应用
The Moth-Flame Optimization based Adaptive Sparsest Time-Frequency Analysis Method and its Application
摘要
Abstract
A complicated signal can be decomposed by using the adaptive and sparsest time-frequency analysis (ASTFA) method. However, the initial phase function and bandwidth parameter is chosen by experience. The decomposition ability of the ASTFA method is severely affected if the initial phase function and bandwidth parameter are chosen inappropriately. Aiming at this drawback of ASTFA, in this paper the moth-flame optimization (MFO) algorithm is applied to optimize the phase function and bandwidth parameter. The moth-flame optimization based adaptive and sparsest time-frequency analysis (MFO-ASTFA) method is put forward. The MFO-ASTFA method has been compared with ASTFA. Furthermore, the MFO-ASTFA has been applied to the gear fault diagnosis. The results has shown the superiority and effectiveness of the MFO-ASTFA method.关键词
故障诊断/自适应最稀疏时频分析/蛾火优化算法/齿轮Key words
fault diagnosis/adaptive and sparsest time-frequency analysis (ASTFA)/moth-flame optimization algorithm/gear分类
机械制造引用本文复制引用
程正阳,王荣吉,杨兴凯,程军圣..基于蛾火优化的自适应最稀疏时频分析方法及应用[J].噪声与振动控制,2019,39(5):185-190,6.基金项目
国家自然科学基金资助项目(51875183) （51875183）
湖南省重点研发计划资助项目(2017GK2182) （2017GK2182）
