With a view to completing the fault diagnosis of rotating machinery efficiently and accurately, this paper presents a novel fault diagnosis model that combines multivariate hierarchical multiscale fluctuation dispersion entropy (MHMFDE), multi-cluster feature selection (MCFS) and gray wolf optimization-based kernel extreme learning machine(GWO-KELM). Firstly, MHMFDE is presented to capture the high-dimensional fault features hidden in the attained multichannel vibration signals. Integrating the multiscale entropy-based method and the hierarchical entropy-based method that are currently popular in the domain of fault identification, MHMFDE can simultaneously extract affluent fault features from multivariate vibration signals in-depth as well as overcoming the problem of information loss in the existing single-channel data analysis methods. Afterward, MCFS is used to pick sensitive features from the attained raw fault features to form the sensitive feature vectors, thereby reducing the impact of redundant features. Finally, GWO-KELM is adopted to quantitatively analyze the diagnostic effect. Three examples reveal that the presented approach enjoys excellent performance in the fault diagnosis domain. Especially for the identification of compound faults of rotating machinery, the performance of the presented method is significantly superior to that of existing methods.

为了高效准确地完成旋转机械的故障诊断，本文提出了一种结合多元层次多尺度波动分散熵（MHMFDE）、多聚类特征选择（MCFS）和基于灰狼优化的内核的新型故障诊断模型。极限学习机（GWO-KELM）。首先，MHMFDE 被用来捕获隐藏在获得的多通道振动信号中的高维故障特征。MHMFDE融合了当前故障识别领域流行的多尺度熵方法和基于层次熵的方法，可以同时从多元振动信号中深度提取丰富的故障特征，克服信息丢失问题。现有的单通道数据分析方法。之后，MCFS 用于从获得的原始故障特征中挑选敏感特征形成敏感特征向量，从而减少冗余特征的影响。最后采用GWO-KELM对诊断效果进行定量分析。三个例子表明，所提出的方法在故障诊断领域具有出色的性能。特别是对于旋转机械复合故障的识别，所提方法的性能明显优于现有方法。三个例子表明，所提出的方法在故障诊断领域具有出色的性能。特别是对于旋转机械复合故障的识别，所提方法的性能明显优于现有方法。三个例子表明，所提出的方法在故障诊断领域具有出色的性能。特别是对于旋转机械复合故障的识别，所提方法的性能明显优于现有方法。