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Multivariate hierarchical multiscale fluctuation dispersion entropy: Applications to fault diagnosis of rotating machinery

机译：多变量等级多尺度波动分散熵：应用于旋转机械故障的应用

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摘要

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. (C) 2021 Elsevier Ltd. All rights reserved.
机译：本文旨在完成旋转机械的故障诊断，本文提出了一种新的故障诊断模型，将多变量分层多尺度波动分散熵（MHMFDE），多集群特征选择（MCF）和基于灰狼优化的内核相结合极端学习机（GWO-KELM）。首先，提出MHMFDE以捕获所达到的多通道振动信号中隐藏的高维故障特征。集成了基于MultiScale熵的方法和基于分层熵的方法，目前在故障识别领域中流行，MHMFDE可以同时从多变量振动信号中提取富裕的故障特征，并克服信息丢失问题现有的单通道数据分析方法。之后，MCFS用于从达到的原始故障特征挑选敏感功能以形成敏感特征向量，从而降低冗余功能的影响。最后，采用GWO-KELM定量分析诊断效果。三个例子表明，本方法在故障诊断域中享有出色的性能。特别是对于旋转机械复合故障的识别，所提出的方法的性能显着优于现有方法。 （c）2021 elestvier有限公司保留所有权利。

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