基于欠阻尼混合势随机共振的微弱信号检测方法  

Weak signal detection method based on underdamped mixed potential stochastic resonance

作　　者：任学平[1] 李飞 李志星[1,2] REN Xue-ping;LI Fei;LI Zhi-xing(School of Mechanical Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China;Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles,Beijing University of Civil Engineering and Architecture,Beijing 102612,China)

机构地区：[1]内蒙古科技大学机械工程学院,内蒙古包头014010 [2]北京建筑大学城市轨道交通车辆服役性能保障北京市重点实验室,北京102612

出　　处：《机电工程》2022年第10期1365-1373,共9页Journal of Mechanical & Electrical Engineering

基　　金：内蒙古自治区自然科学基金项目(2019LH05008)。

摘　　要：针对强噪声背景下机械设备微弱信号的检测问题,即其微弱故障的诊断问题,提出了一种自适应噪声完备集合经验模态分解(CEEMDAN)—小波阈值联合降噪方法与欠阻尼混合势随机共振(UMPSR)相结合的微弱信号检测方法。首先,建立了欠阻尼混合势随机共振模型,描述了势函数的特点,从理论上推导出了系统的输出信噪比,并分析了在不同参数下,信噪比和噪声强度的关系;然后,对原始信号进行了预处理,将降噪后的重构信号输入系统模型,利用自适应模拟退火粒子群算法对系统参数进行了优化,实现了随机共振系统的最佳匹配;最后,将所提方法应用于仿真故障信号和滚动轴承内圈故障的实验中,并将其结果与采用混合势随机共振(MPSR)方法所得结果进行了对比。研究结果表明:当故障频率为50 Hz和212.85 Hz时,相比于混合势随机共振方法,经欠阻尼混合势随机共振处理后的故障频率处的频谱峰值更高,且噪声干扰更少;该结果可以有效地提高滚动轴承故障信号检测能力。Aiming at the diagnosis of faint faults in mechanical equipment under strong noise background,a weak signal detection method of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)-wavelet threshold noise reduction method combined with underdamped mixed potential stochastic resonance(UMPSR)was proposed.Firstly,an underdamped mixed potential stochastic resonance model was established,the characteristics of the potential function were described,the output signal-to-noise ratio of the system was derived theoretically and the relationship between the signal-to-noise ratio and the noise intensity under different parameters was analyzed.Then,the original signal was preprocessed,the reconstructed signal from the noise reduction method was input into the system model and the system parameters were optimized by using the adaptive simulated annealing particle swarm algorithm to achieve the best matching of the stochastic resonant system.Finally,the proposed method was applied to the experiment of simulating the fault signal and the actual rolling bearing inner ring fault signal.The results were compared with those obtained by the mixed potential stochastic resonance(MPSR)method.The research results show that compared with the mixed-potential stochastic resonance method,the proposed underdamped mixed potential stochastic resonance method has higher spectral peaks and less disturbed by noise at fault frequencies of 50 Hz and 212.85 Hz,which can effectively improve the rolling bearing fault signal detection capability.

关 键 词：机械故障诊断 自适应噪声完备集合经验模态分解 小波阈值联合降噪 欠阻尼混合势随机共振 噪声强度 信噪比 频谱峰值 

分 类 号：TH133.33[机械工程—机械制造及自动化] TH17