A Novel Feature for Fault Classification of Rotating Machinery: Ternary Approximate Entropy for Original, Shuffle and Surrogate Data

A Novel Feature for Fault Classification of Rotating Machinery: Ternary Approximate Entropy for Original, Shuffle and Surrogate Data

Existing works have paid scant attention to the multivariate entropy of complex data. Thus, existing methods perform poorly in fully exposing the nature of complex data. To mine a rich vein of data features, this paper applies a shuffle and surrogate approach to complex data to decouple probability...

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Bibliographic Details
Main Authors: Chunhong Dou, Jinshan Lin, Lijun Guo
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Machines
Subjects:
rotating machinery
fault classification
approximate entropy
shuffle data
surrogate data
Online Access:https://www.mdpi.com/2075-1702/11/2/172

Internet

https://www.mdpi.com/2075-1702/11/2/172

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