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

作　　者：程正阳 王荣吉[1] 杨兴凯 程军圣[2] CHENG Zhengyang;WANG Rongji;YANG Xingkai;CHENG Junsheng(College of Mechanical and Electrical Engineering,Central South University of Forestry and Technology,Changsha 410004,China;College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China)

机构地区：[1]中南林业科技大学机电工程学院,长沙410004 [2]湖南大学机械与运载工程学院,长沙410082

出　　处：《噪声与振动控制》2019年第5期185-190,共6页Noise and Vibration Control

基　　金：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)

摘　　要：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 timefrequency 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 

分 类 号：TH165[机械工程—机械制造及自动化]