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Title: Bearing Fault Detection and Wear Estimation Using Machine Learning

Authors:
ORCiD logo [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1557163
Report Number(s):
LA-UR-19-27700
DOE Contract Number:  
89233218CNA000001
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
Machine Learning Bearing Fault Detection Predictive Maintenance Wear Estimation K-means Support Vector Machine

Citation Formats

Jenkins, Cody David. Bearing Fault Detection and Wear Estimation Using Machine Learning. United States: N. p., 2019. Web. doi:10.2172/1557163.
Jenkins, Cody David. Bearing Fault Detection and Wear Estimation Using Machine Learning. United States. doi:10.2172/1557163.
Jenkins, Cody David. Thu . "Bearing Fault Detection and Wear Estimation Using Machine Learning". United States. doi:10.2172/1557163. https://www.osti.gov/servlets/purl/1557163.
@article{osti_1557163,
title = {Bearing Fault Detection and Wear Estimation Using Machine Learning},
author = {Jenkins, Cody David},
abstractNote = {},
doi = {10.2172/1557163},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}

Technical Report:

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