Bearing fault detection through machine learning: Time-domain vs time-frequency analysis for feature extraction

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Original language	English
Title of host publication	1st EAGE Workshop on High Performance Computing for Upstream in Latin America
DOIs	https://doi.org/10.3997/2214-4609.201803075
Publication status	Published - 2019
Externally published	Yes

Keywords

This output contributes to the following UN Sustainable Development Goals (SDGs)

@inproceedings{32f4e53612f2462ba0a371217e8f1bad,

title = "Bearing fault detection through machine learning: Time-domain vs time-frequency analysis for feature extraction",

keywords = "NLA, Maintenance engineering, Smart Retrofitting, Industry 4.0, Smart Maintenance",

author = "F. Ulloa and G. Barbieri",

year = "2019",

doi = "10.3997/2214-4609.201803075",

language = "English",

booktitle = "1st EAGE Workshop on High Performance Computing for Upstream in Latin America",

}

TY - GEN

T1 - Bearing fault detection through machine learning: Time-domain vs time-frequency analysis for feature extraction

AU - Ulloa, F.

AU - Barbieri, G.

PY - 2019

Y1 - 2019

KW - NLA

KW - Maintenance engineering

KW - Smart Retrofitting

KW - Industry 4.0

KW - Smart Maintenance

UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85084020619&partnerID=MN8TOARS

U2 - 10.3997/2214-4609.201803075

DO - 10.3997/2214-4609.201803075

M3 - Conference contribution

BT - 1st EAGE Workshop on High Performance Computing for Upstream in Latin America

ER -