基于GSO算法的自适应随机共振轴承故障诊断
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摘要
针对强噪声下轴承故障弱信号较难检测和传统仅靠单参数优化随机共振系统问题,提出一种基于萤火虫优化算法(GSO)的自适应随机共振轴承故障信号检测方法。首先按固定频率压缩比压缩频率;然后以传统随机共振系统输出信噪比作为GSO算法的初始荧光素,利用GSO算法选取随机共振系统的结构参数a、b;最后通过双稳随机共振系统的输出信噪比检测轴承故障弱信号是否增强,通过系统的输出时域图分析信号的周期性,通过功率谱分析轴承故障弱信号的特征频率。仿真验证与试验验证结果分析表明,该方法可检测出轴承故障弱信号,实现弱信号的增强和降噪。
Aiming at the problems that the weak signals of bearing faults are difficult to detect in strong noise background and the traditional stochastic resonance system only relies on single-parameter optimization, a fault signal detection method of adaptive stochastic resonance bearings based on firefly optimization algorithm(GSO) is proposed.Firstly, the frequency is compressed according to a fixed frequency compression ratio. Then, with the output SNR of the traditional stochastic resonance system as the initial fluorescein of the GSO algorithm, the structural parameters a and b of the stochastic resonance system is selected using GSO algorithm. Finally, the output SNR of the bi-stable stochastic resonance system is used to detect whether the weak signal of the bearing fault is enhanced. The output time-domain diagram of the system is used to analyze the periodicity of the signal. And the power spectrum is used to analyze the characteristic frequency of the weak signal of the bearing fault. Results of simulation and experiment verify that this method can detect weak signals of bearing faults and realize weak signal enhancement and noise reduction.
Aiming at the problems that the weak signals of bearing faults are difficult to detect in strong noise background and the traditional stochastic resonance system only relies on single-parameter optimization, a fault signal detection method of adaptive stochastic resonance bearings based on firefly optimization algorithm(GSO) is proposed.Firstly, the frequency is compressed according to a fixed frequency compression ratio. Then, with the output SNR of the traditional stochastic resonance system as the initial fluorescein of the GSO algorithm, the structural parameters a and b of the stochastic resonance system is selected using GSO algorithm. Finally, the output SNR of the bi-stable stochastic resonance system is used to detect whether the weak signal of the bearing fault is enhanced. The output time-domain diagram of the system is used to analyze the periodicity of the signal. And the power spectrum is used to analyze the characteristic frequency of the weak signal of the bearing fault. Results of simulation and experiment verify that this method can detect weak signals of bearing faults and realize weak signal enhancement and noise reduction.
引文
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[2]邢欣,崔亚辉,刘晓琳,等.一种自适应提取有效信号的滚动轴承故障诊断方法[J].噪声与振动控制,2018,38(2):150-153.
[3]雷亚国,韩冬,林京,等.自适应随机共振新方法及其在故障诊断中的应用[J].机械工程学报,2012,48(7):62-67.
[4]张仲海,王多,王太勇,等.采用粒子群算法的自适应变步长随机共振研究[J].振动与冲击,2013,32(19):125-130.
[5]朱维娜,林敏.基于人工鱼群算法的轴承故障随机共振自适应检测方法[J].振动与冲击,2014,33(6):143-147.
[6]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.
[7]范卫姣,王辅忠,张光璐.滤波器与随机共振结合检测微弱信号[J].应用声学,2015,34(2):169-174.
[8]林敏,黄咏梅,方利民.耦合双稳系统的随机共振控制[J].物理学报,2008,57(4):2048-2052.
[9]李志星,石博强.自适应奇异值分解的随机共振提取微弱故障特征[J].农业工程学报,2017,33(11):60-67.
[10]陆屹,程培源,齐悦,等.基于改进人工萤火虫算法的装配序列规划研究[J].测控技术,2016,35(3):140-144.
[11]李恒,郭星,李炜.基于改进的萤火虫算法的PID控制器参数寻优[J].计算机应用与软件,2017,34(7):227-230.