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Improved Random Forest Fault Diagnosis Model Based on Fault Ratio Cover

Improved Random Forest Fault Diagnosis Model Based on Fault Ratio

International Journal of Advanced Network, Monitoring and Controls
Volume 7 (2022): Issue 2 (January 2022)
By: Ziwei Ding and  Shunyuan Huang  
Open Access
|May 2023

Authors

Ziwei Ding

146377997@qq.com

School of Computer Science and Engineering, Xi'an Technological University, Xi'an, China
State and Provincial Joint Engineering, Lab of Advanced Network, Monitoring and Control, Xi'an, China

Shunyuan Huang

sylvianoemie@sina.com

School of Computer Science and Engineering, Xi'an Technological University, Xi'an, China
State and Provincial Joint Engineering, Lab of Advanced Network, Monitoring and Control, Xi'an, China
DOI: https://doi.org/10.2478/ijanmc-2022-0019 | Journal eISSN: 2470-8038
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Language: English
Page range: 85 - 91
Published on: May 26, 2023
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Complex Equipment,
Fault Diagnosis,
Random Forest,
Unbalanced Data
Related subjects:
Computer sciences,
Computer sciences, other

© 2023 Ziwei Ding, Shunyuan Huang, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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