摘要
针对传统随机共振只能单参数优化,且随机共振(Stochastic Resonance,SR)只能处理小参数的故障信号,提出了一种基于果蝇优化算法(Fruit Fly Optimization Algorithm,FOA)的自适应随机共振提取滚动轴承故障特征的方法。首先利用FOA优化双稳系统结构参数,进行自适应随机共振,达到最佳随机共振,实现时频增强的目的,再经过变分模式分解(Variational Mode Decomposition,VMD)分解,选取合适IMF分量进行重构;最后对重构信号进行倒频谱,可明显观察到故障特征频率。仿真与实际数据的分析验证了该方法的有效性和优越性。
The traditional stochastic resonance can only optimize single parameter,and the stochastic resonance(SR)can only deal with fault signals with small parameters.In order to avoid this diadwantages,an adaptive stochastic resonance based on Fruit Fly Optimization Algorithm(FOA)is proposed which is a method for extracting fault characteristics of rolling bearings.Firstly,FOA is used to optimize the bistable system structural parameters that are used by adaptive stochastic resonance,so the best stochastic resonance and the purpose of time-frequency enhancement are achieved.Then,the stochastic resonance output signal is decomposed by Variational Mode Decomposition(VMD)and the appropriate IMF components are selected for reconstruction.Finally,cepstrum is applied to the reconstructed signal,and the fault feature frequency can be clearly observed.The analysis of simulation and actual data verify the effectiveness and superiority of the method.
作者
许梦颖
王红军
XU Meng-ying;WANG Hong-jun(School of Mechanical and Electrical Engineering,Beijing Information Science and Technology University,Beijing 100192,China)
出处
《组合机床与自动化加工技术》
北大核心
2019年第2期94-96,99,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
国家自然科学基金项目(51575055)
关键词
FOA
自适应随机共振
倒频谱
特征频率
FOA
adaptive stochastic resonance
cepstrum
characteristic frequency
