多元微积分
一致性(知识库)
可靠性(半导体)
星团(航天器)
融合
计算机科学
可靠性工程
统计
数学
统计物理学
物理
人工智能
工程类
热力学
控制工程
哲学
功率(物理)
语言学
程序设计语言
作者
HANG GUO,Xianzhi Wang,Hongbo Ma,Gaige Chen,Yongbo Li
标识
DOI:10.1088/1361-6501/ad42c2
摘要
Abstract Recent researches have shown that the multivariable entropy based feature extraction method can obtain better diagnosis results for fault diagnosis of planetary gearbox. However, the nature properties of multivariable entropy have still not been deeply explored: the reliability of multi-source information fusion and cluster consistency for the same fault signal. These two properties will affect the accuracy of fault diagnosis based on multivariate entropy. This paper aims to reveal the nature properties of multivariate entropy. Firstly, a rigid-flexible coupling dynamic model of a planetary gearbox is conducted to establish a pure test environment. Then the generated vibration signals are used to evaluate the fusion reliability and cluster consistency of multivariable entropy. Additionally, a new multivariable entropy feature extraction method called variational embedding refined composite multiscale diversity entropy (veRCMDE) is proposed. Finally, the simulation and experiment results show that high fusion reliability and high cluster consistency enable multivariate entropy to extract more valuable features, and the proposed veRCMDE performs the best in all experiments.
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