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Efficient feature selection for enhanced chiller fault diagnosis:A multi-source ranking information-driven ensemble approach

Zhanwei Wang Penghua Xia Jingjing Guo Sai Zhou Lin Wang Yu Wang Chunxiao Zhang

建筑模拟（英文版）2025，Vol.18Issue(1)：141-159,19.

建筑模拟（英文版）2025，Vol.18Issue(1)：141-159,19.DOI:10.1007/s12273-024-1199-1

Efficient feature selection for enhanced chiller fault diagnosis:A multi-source ranking information-driven ensemble approach

Zhanwei Wang ¹Penghua Xia ¹Jingjing Guo ¹Sai Zhou ¹Lin Wang ¹Yu Wang ¹Chunxiao Zhang¹

作者信息

1. Institute of Building energy and Thermal Science,Henan University of Science and Technology,Luoyang 471023,China||Henan Provincial Engineering Research Center of Building Environmental Control and Safety,Luoyang 471023,China
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摘要

关键词

chiller/feature selection/fault diagnosis/multi-source ranking information/machine learning

Key words

chiller/feature selection/fault diagnosis/multi-source ranking information/machine learning

引用本文复制引用

Zhanwei Wang,Penghua Xia,Jingjing Guo,Sai Zhou,Lin Wang,Yu Wang,Chunxiao Zhang..Efficient feature selection for enhanced chiller fault diagnosis:A multi-source ranking information-driven ensemble approach[J].建筑模拟（英文版）,2025,18(1):141-159,19.

基金项目

The authors gratefully acknowledge the support of the National Natural Science Foundation of China(No.52478087),China Postdoctoral Science Foundation(No.2024M750799,No.2024T170238)and China Scholarship Council(No.202308410494),Zhongyuan Outstanding Youth Talent Program(No.2022 Year),Youth Scientist Project in Henan Province(No.225200810087),the Program for Science&Technology Innovation Talents in Universities of Henan Province(No.22HASTIT025),and the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(No.22IRTSTHN006). （No.52478087）

建筑模拟（英文版）

ISSN：1996-3599

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