摘要
针对于弱信号在齿轮故障中难以提取问题,提出了一种基于级联双稳随机共振(Cascaded Bistable Stochastic Resonance,CBSR)降噪和局部均值分解(Local Mean Decomposition,LMD)齿轮故障的诊断方法。随机共振可有效削弱信号中的噪声,利用噪声增强故障信号的微弱特征;LMD方法可自适应将复杂信号分解为若干个具有一定物理意义上PF分量之和,适合处理多分量调幅调频信号。首先将振动信号进行CBSR消噪处理,然后对消噪信号进行LMD分解,通过PF分量的幅值谱找到齿轮的故障频率。通过齿轮磨损故障诊断的工程应用,表明该方法可以有效提取齿轮故障微弱特征,实现齿轮箱的早期故障诊断。
Aiming at the difficulty of extracting the weak signal in gear fault diagnosis,a method for gear fault diagnosis based on cascaded bistable stochastic resonance（ CBSR） denoising and local mean decomposition（ LMD） was proposed. The technique of stochastic resonance can remove noise in signals effectively and make use of noise to strengthen the weak fault feature; LMD can decompose a complicated signal into several stationary PF（ product function）components with reality meanings, so it is very suitable to analyze the multi-component amplitude-modulated and frequency-modulated signals. Here,the CBSR was employed in the pretreatment to remove noise in vibration signals,the denoised signal was decomposed with LMD,and then the fault frequency of gear was found by inspecting the amplitude spectra of PF components. The engineering application of the method in fault diagnosis of gear wear demonstrated that it can extract the weak feature of gear fault effectively and realize the early gear fault diagnosis.
出处
《振动与冲击》
EI
CSCD
北大核心
2015年第5期95-101,共7页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(10772061)
关键词
级联双稳随机共振
局部均值分解
故障诊断
齿轮
cascaded bistable stochastic resonance
local mean decomposition
fault diagnosis
gear
