级联双稳随机共振和基于hermite插值的局部均值分解方法在齿轮故障诊断中应用

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，简称CBSR)降噪和局部均值分解（Local Mean Decomposition，简称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。  

Application of Cascaded Bistable Stochastic Resonance and Hermite Interpolation Local Mean Decomposition Method in Gear Fault Diagnosis

Based on the difficulty of extracting the weak signal in gear fault diagnosis, the method of gear fault diagnosis based on cascaded bistable stochastic resonance（CBSR）denoising and local mean decomposition（LMD）was studied . Stochastic resonance can remove noise in the signals effectively and make use of noise to strengthen the weak fault feature ;The complicated signal can be decomposed into several stationary PF (product function) components with reality meanings by LMD, so it is very suitable to analyze the multi-component amplitude-modulated and frequency-modulated signal. First CBSR was employed as the pretreatment to remove noise in vibration signals and then the denoised signal was decomposed by LMD, the fault frequency of gear was found through the amplitude spectrums of the PF components. Through the engineering application of the fault diagnosis on gear wear demonstrated that this method can extracting the weak feature of gear fault effectively and realize the early gear fault diagnosis.