MCKD最佳故障周期搜索的齿轮箱故障特征提取
Optimum Fault Period Searching of MCKD Algorithm for Fault Feature Extraction of Gearbox

针对最小解熵解卷积（Minimum entropy deconvolution，MED）算法易受强噪声和野值的影响，引出了最大相关峭度解卷积（Maximum correlated kurtosis deconvolution，MCKD）的齿轮箱故障特征提取方法，克服了MED算法的不足。然而凭先验信息选取的故障周期，可能导致MCKD解卷积效果很差，因此提出了MCKD算法的最佳故障周期搜索思路，即在合适的滤波器阶数L下，最佳故障周期的搜索可以限定于理论计算周期左右的某一范围内，使不同步距M关于最佳周期的最大相关峭度达到全局最优，以确保了MCKD算法具有良好的解卷积效果。断齿与局部断齿故障特征提取试验结果佐证了最佳故障周期搜索思路的可行性及其效果。
Considering this problem that minimum entropy deconvolution (MED) algorithm is unsuitable for strong noise and outliers, a new method of fault feature extraction from gearbox based on maximum correlated kurtosis deconvolution (MCKD) is introduced, which can overcome the shortcoming of MED algorithm. However, the effect of MCKD algorithm is probably poor according to priori information to select fault period. Therefore, an idea of fault period searching is discussed, the fault period can be limited to a certain range of computation period, and the maximum correlated kurtosis(KC) converges to the global maximum about the optimum fault period with large M and suitable L, and ensures the effective results for MCKD algorithm with different M values. The experimental results of gearbox fault feature extraction with a missing tooth and a chipped tooth indicate that the feasibility and effectiveness of optimum fault period searching of MCKD method are testified