摘要
滚动轴承振动信号能够及时准确地提供机电设备状态特征信息,且可实现在线或离线监测,广泛用于滚动轴承故障诊断。由于滚动轴承工作环境复杂多变,往往掺杂较多噪声,噪声会淹没机电设备状态的有用特征信息。针对传统小波阈值函数对轴承信号降噪不明显的问题,提出了一种用于轴承振动信号降噪的差分进化优化小波软阈值算法,对含噪信号进行小波分解,利用广义交叉验证GCV函数作为新的阈值函数对分解后的小波系数进行处理,结合差分进化算法进行寻优获取最优阈值。实验采用美国凯斯西储大学的轴承数据进行仿真分析,通过与常用降噪方法相比,该方法在较好地保留特征信号的前提下,较大程度地去除了噪声,有效地提高了降噪效果。
The rolling bearing vibration signal can provide timely and accurate information on the status characteristics of electromechanical equipment and can realize online or offline monitoring,which is widely used in fault diagnosis of rolling bearings.Due to the complex and changing working environment of rolling bearings,they are often mixed with a lot of noise,which can drown out the useful feature information of electromechanical equipment status.As the traditional wavelet thresholding function cannot greatly reduce the noise in bearing signals,this paper proposes a wavelet soft-thresholding algorithm optimized by differential evolution(DE)to suppress the noise in bearing vibration signals.The algorithm performs wavelet decomposition on the noisy signal,uses the generalized cross-validation(GCV)function as the new thresholding function to process the decomposed wavelet coefficients,and employs the DE algorithm to seek the optimal threshold.The experiments use the bearing data of Case Western Reserve University for simulation analysis.Compared with the commonly used denoising methods,the method in this paper can effectively improve the denoising effect by removing the noise to a large extent while well retaining the characteristic signal.
作者
覃坚
费太勇
曲智国
张逸楠
QIN Jian;FEI Taiyong;QU Zhiguo;ZHANG Yinan(Air Force Early Warning Academy,Wuhan 430019,China)
出处
《现代防御技术》
北大核心
2023年第2期141-147,共7页
Modern Defence Technology
关键词
故障诊断
滚动轴承振动信号
噪声
小波阈值法
差分进化算法
GCV函数
fault diagnosis
rolling bearing vibration signal
noise
wavelet thresholding method
differential evolution(DE)algorithm
generalized cross-validation(GCV)function
