A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: in...

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Bibliographic Details
Main Authors: Jianpeng Ma, Song Han, Chengwei Li, Liwei Zhan, Guang-zhu Zhang
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Entropy
Subjects:
rolling bearing
fault diagnosis
signal denoising
intrinsic time-scale decomposition
coyote optimization algorithm
generalized refined composite multiscale sample entropy
Online Access:https://www.mdpi.com/1099-4300/23/4/451

Internet

https://www.mdpi.com/1099-4300/23/4/451

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