Fault Feature Extraction and Diagnosis of Rolling Bearings Based on Enhanced Complementary Empirical Mode Decomposition with Adaptive Noise and Statistical Time-Domain Features

In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white noise...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhan, Liwei, Ma, Fang, Zhang, Jingjing, Li, Chengwei, Li, Zhenghui, Wang, Tingjian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767346/
https://www.ncbi.nlm.nih.gov/pubmed/31546904
http://dx.doi.org/10.3390/s19184047

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767346/
https://www.ncbi.nlm.nih.gov/pubmed/31546904
http://dx.doi.org/10.3390/s19184047

Ejemplares similares