基于EEMD与GWO-MCKD的门座起重机回转支承故障诊断

低速重载的门座起重机回转支承信号易受环境噪声影响，难以提取故障特征。为解决此问题，提出一种集合经验模态分解（EEMD）与灰狼优化（GWO）算法优化的最大相关峭度解卷积（MCKD）相结合的故障诊断方法。对回转支承信号进行EEMD分解，得到一系列本征模态函数（IMF），选择峭度最大的IMF作为最优分量；以相关峭度为目标函数，利用GWO寻找在最优分量上的MCKD的最佳参数组合；使用最佳参数组合的MCKD对最优分量进行降噪，突出故障冲击成分；对降噪后的信号进行包络谱分析，完成故障诊断。结果表明：所提方法能自适应增强故障冲击成分，有效提取故障特征。

Fault Diagnosis of Slewing Bearing of Portal Crane Based on EEMD and GWO-MCKD

The low-speed and heavy-load portal crane slewing bearing signal is easily affected by environmental noise and it is difficult to extract fault characteristics.To solve this problem,a fault diagnosis method combining the ensemble empirical mode decomposition (EEMD) and the maximum correlated kurtosis deconvolution (MCKD) optimized by grey wolf optimization (GWO) algorithm was proposed.The slewing bearing signal was decomposed by using EEMD to obtain a series of intrinsic mode functions (IMF),and the IMF with max kurtosis was selected as the optimal component;the correlation kurtosis was taken as the objective function and the GWO was used to find the optimal parameter combination of MCKD on the optimal component;the MCKD of the optimal parameter combination was used to reduce the noise of the optimal component and highlight the fault impact component;the signal after noise reduction was analyzed by using the envelope spectrum,and the fault diagnosis was completed.The results show that by using the proposed method,the fault impact component can be enhanced adaptively,and the fault characteristics can be extracted effectively.