噪声与振动控制2019，Vol.39Issue(3)：199-203,5.DOI:10.3969/j.issn.1006-1355.2019.03.038
基于GSO算法的自适应随机共振轴承故障诊断
Fault Diagnosis of Adaptive Stochastic Resonance Bearings based on GSO Algorithm
方宇 1袁丛振 1胡定玉2
作者信息
- 1. 上海工程技术大学 城市轨道交通学院,上海 201620
- 2. 上海工程技术大学 城市轨道交通学院,上海 201620
- 折叠
摘要
Abstract
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.关键词
振动与波/轴承故障/随机共振/GSO算法/信噪比/特征频率Key words
vibration and wave/ bearing failure/ stochastic resonance/ GSO algorithm/ SNR/ characteristic frequency分类
交通工程引用本文复制引用
方宇,袁丛振,胡定玉..基于GSO算法的自适应随机共振轴承故障诊断[J].噪声与振动控制,2019,39(3):199-203,5.
