期刊论文

The Moth-Flame Optimization based Adaptive Sparsest Time-Frequency Analysis Method and its Application

中文

噪声与振动控制

1006-1355

2019

39

5

185-190

10.3969/j.issn.1006-1355.2019.05.035

国家自然科学基金资助项目(51875183) 湖南省重点研发计划资助项目(2017GK2182)。

本校为第一机构

自适应最稀疏时频分析(Adaptive and sparsest time-frequency analysis,ASTFA)方法能对复杂信号进行自适 应的分解,但是初始相位函数和带宽参数取值需要人工经验,如果选择不当会严重影响ASTFA方法的分解能力。针对 该问题,论文将蛾火优化(Moth-FlameOptimization,MFO)算法应用于ASTFA方法的初始相位函数和带宽参数的优化, 提出基于蛾火优化的自适应最稀疏时频分析(Moth-flame optimization based adaptive sparsest time-frequency analysis, MFO-ASTFA)方法。将MFO-ASTFA与ASTFA方法进行了对比,并将MFO-ASTFA方法应用于齿轮故障诊断,结果表 明了MFO-ASTFA的优越性及有效性。

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) m...