基于RACS-TFODF自适应随机共振系统的轴承故障诊断应用

Bearings Fault Diagnosis of Adaptive Stochastic Resonance Based on RACS-TFODF

针对传统随机共振系统(Stochastic resonance,SR)的参数选择和未考虑历史信息影响随机共振效果的问题,提出一种基于改进布谷鸟算法(Ranking-based Adaptive Cuckoo Search,RACS)的自适应改进势模型随机共振方法。首先,对大参数信号进行移频尺度变换处理,使其满足SR的绝热近似理论要求;其次,提出一种时延分数阶偏置非线性过阻尼随机共振系统(Time-delayed Overdamped Stochastic Resonance System with Fractional Deflection Nonlinearity,TFODF-SR),并研究势模型参数对随机共振效果的影响;进而利用以信噪比作为评价函数的RACS算法自适应确定随机共振系统的结构参数;最后经过时、频域分析提取出滚动轴承故障特征。通过仿真与实测实验分析对所提出方法相比于传统SR系统及没有引入时延反馈项的ODF系统(Overdamped System with Fractional Deflection Nonlinearity,ODF)在滚动轴承故障提取上的有效性和优越性进行验证。

Aiming at the problem in the parameter selection of the traditional stochastic resonance(SR)without considering the effect of historical information on stochastic resonance,an adaptive improved potential model stochastic resonance method based on ranking-based adaptive cuckoo search algorithm(RACS)is proposed.Firstly,the large parameter signal is processed by frequency shift scaling to meet the requirements of adiabatic approximation theory of SR.Then,a time-delayed overdamped stochastic resonance system with fractional deflection nonlinearity(TFODF-SR)is proposed and the result of potential model parameters effect on stochastic resonance is analyzed.With the signal-to-noise ratio(SNR)as the evaluation function,the RACS algorithm is used to optimize the structural parameters of the SR system.The simulation and experimental results verify the effectiveness and superiority of the proposed method in rolling bearing fault extraction compared with the results of the classic SR system and the ODF system without time-delay feedback.