基于FOA的自适应随机共振滚动轴承故障检测
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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.
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.
引文
[1]崔伟成,李伟,孟凡磊,等.基于果蝇优化算法的自适应随机共振轴承故障信号检测方法[J].振动与冲击,2016,35(10):96-100.
[2]崔颖,赵军,赖欣欢.基于EEMD和变尺度随机共振的轴承故障诊断[J].测控技术,2013,32(7):15-18.
[3]王俊.基于多尺度非线性状态特征增强的旋转机械故障诊断方法研究[M].合肥:中国科学技术大学,2015.
[4]樊养余,李利品,党瑞荣.基于随机共振的任意大频率微弱信号检测方法研究[J].仪器仪表学报,2013,34(3):566-572.
[5]何园园,张超,陈帅.自适应随机共振与ELMD在轴承故障诊断中的应用[J].机械科学与技术,2017,37(4):607-613.
[6]张俊甲,马增强,王梦奇.基于VMD与自相关分析的滚动轴承故障特征提取[J].电子测量与仪器学报,2017,31(9):1372-1378.
[7]王志霞,郭利,程茜茜.基于VMD的自适应随机共振在滚动轴承早期故障检测中的应用[J].机械传动,2018,42(4):144-149.
[8]钱林,康敏,傅秀清.基于VMD的自适应形态学在轴承故障诊断中的应用[J].振动与冲击,2017,36(3):227-233.
[9]袁婧怡,宋鹏.基于VMD和双谱的风电机组滚动轴承故障特征提取[J].华北电力技术,2017(10):50-56.
[10]牟竹青,冯早,黄国勇,等.自适应随机共振和DEMD的单向阀早期故障诊断[J].机械科学与技术,2017,37(4):537-544.
[11]朱朔,白瑞林,刘秦川.基于果蝇优化算法_小波支持向量机数据描述的滚动轴承性能退化评估[J].中国机械工程,2018,29(5):602-608.
[12]张西宁,李兵,雷威.一种改进的局部倒频谱分析方法[J].西安交通大学学报,2016,50(8):1-5.
[2]崔颖,赵军,赖欣欢.基于EEMD和变尺度随机共振的轴承故障诊断[J].测控技术,2013,32(7):15-18.
[3]王俊.基于多尺度非线性状态特征增强的旋转机械故障诊断方法研究[M].合肥:中国科学技术大学,2015.
[4]樊养余,李利品,党瑞荣.基于随机共振的任意大频率微弱信号检测方法研究[J].仪器仪表学报,2013,34(3):566-572.
[5]何园园,张超,陈帅.自适应随机共振与ELMD在轴承故障诊断中的应用[J].机械科学与技术,2017,37(4):607-613.
[6]张俊甲,马增强,王梦奇.基于VMD与自相关分析的滚动轴承故障特征提取[J].电子测量与仪器学报,2017,31(9):1372-1378.
[7]王志霞,郭利,程茜茜.基于VMD的自适应随机共振在滚动轴承早期故障检测中的应用[J].机械传动,2018,42(4):144-149.
[8]钱林,康敏,傅秀清.基于VMD的自适应形态学在轴承故障诊断中的应用[J].振动与冲击,2017,36(3):227-233.
[9]袁婧怡,宋鹏.基于VMD和双谱的风电机组滚动轴承故障特征提取[J].华北电力技术,2017(10):50-56.
[10]牟竹青,冯早,黄国勇,等.自适应随机共振和DEMD的单向阀早期故障诊断[J].机械科学与技术,2017,37(4):537-544.
[11]朱朔,白瑞林,刘秦川.基于果蝇优化算法_小波支持向量机数据描述的滚动轴承性能退化评估[J].中国机械工程,2018,29(5):602-608.
[12]张西宁,李兵,雷威.一种改进的局部倒频谱分析方法[J].西安交通大学学报,2016,50(8):1-5.