【摘要】 针对复杂环境下的滚动轴承故障信息有效提取与辨识问题，提出一种基于特征模态分解(feature mode decomposition, FMD)及多尺度模糊散布熵(multiscale fuzzy dispersion entropy, MFDE)和斑马优化算法(zebra optimization algorithm, ZOA)优化支持向量机的滚动轴承故障诊断方法。为了解决FMD中关键参数不具有自适应性这一问题，以最小包络熵作为目标函数，采用白鲸优化算法(beluga whale optimization, BWO)优化FMD寻找最优参数组合，实现对故障信号的最优分解；引入多尺度模糊散布熵构建分解后不同模态下的特征向量；最后，将特征向量输入支持向量机中进行训练和识别，通过公开数据集和自制实验平台数据集验证了提出方法的有效性。更多还原

【Abstract】 Aiming at the problem of effective extraction and identification of rolling bearing fault information in complex environments, a fault diagnosis method for rolling bearings based on feature mode decomposition(FMD) combined with multiscale fuzzy dispersion entropy(MFDE) and zebra optimization algorithm(ZOA) optimization support vector machine was proposed. In order to solve the problem that the key parameters in FMD are not adaptive, the minimum envelope entropy was used as the objective function, and the beluga whale optimization(BWO) was used to optimize FMD to find the optimal parameter combination to achieve the optimal decomposition of fault signals. Multiscale fuzzy dispersion entropy was introduced to construct the eigenvectors under different modes after decomposition. Finally, the feature vectors were input into the support vector machine for training and recognition. The effectiveness of the proposed method was verified by the public dataset and the self-made experimental platform dataset.更多还原