摘要
针对现有小波去噪方法存在阈值函数中未知参数选取依赖经验,导致去噪不充分或去噪后信号失真的问题。提出了一种基于相对小波熵(RWE)的粒子群优化(PSO)算法,用于小波阈值函数中未知参数的自适应寻优,达到滚动轴承振动信号自适应降噪目的。改进了一种含两未知参数的小波阈值函数;以相对小波熵为优化算法的适应度函数对未知参数进行自适应寻优,得到最优小波阈值函数;通过对模拟仿真信号和试验采集的滚动轴承振动信号进行分析。结果表明:优化后的小波去噪方法能够更好地将噪声从染噪信号中滤除,去噪后信号波形的平滑度更好,信噪比相较硬阈值去噪提高29.4%,而且保留了原始信号更多的细节特征,具有更好的去噪性能和应用实用价值。
In view of the existing wavelet threshold denoising method that unknown parameters selection in existing functions depends on experience and brings about insufficient denoising or signal distortion after denoising, a particle swarm optimization(PSO) algorithm based on relative wavelet entropy(RWE) was proposed for adaptive optimization of unknown parameters in wavelet threshold function to achieve adaptive noise reduction of rolling bearing vibration signal. A wavelet threshold function with two unknown parameters was improved. The relative wavelet entropy was used as the fitness function to optimize the unknown parameters and the optimal wavelet threshold function was obtained. The vibration signal of rolling bearing acquired by simulation and experiment was analyzed. Results showed that the optimized wavelet denoising method can filter the noise from the denoised signal better, the signal waveform after denoising had better smoothness, the signal-to-noise ratio was 29.4% higher than the hard threshold, and retained more details of the original signal, presenting better denoising performance and practical value.
作者
杨旭
邱明
陈立海
陈月
YANG Xu;QIU Ming;CHEN Lihai;CHEN Yue(School of Mechatronics Engineering,Henan University of Science and Technology,Luoyang Henan 471003,China)
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2020年第11期2339-2347,共9页
Journal of Aerospace Power
基金
国家重点研发发展计划(2018YFB2000203)
国家自然科学基金(51805151)。
关键词
小波阈值去噪
滚动轴承
振动信号
相对小波熵
粒子群优化算法
wavelet threshold denoising
rolling bearing
vibration signal
relative wavelet entropy(RWE)
particle swarm optimization(PSO)algorithm
