Data-driven fault diagnosis for residential variable refrigerant flow system on imbalanced data environments-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Data-driven fault diagnosis for residential variable refrigerant flow system on imbalanced data environments

Published:2021-05 Issue: Volume:125 Page:34-43
ISSN:0140-7007
Container-title:International Journal of Refrigeration
language:en
Short-container-title:International Journal of Refrigeration

Author:

Zhou Zhenxin,Chen Huanxin,Li Guannan,Zhong Hanlu,Zhang Menghua,Wu Junfeng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Mechanical Engineering,Building and Construction

Reference40 articles.

1. Transition to Sustainable Buildings

2. SMOTE: synthetic minority over-sampling technique;Chawla;J. Artif. Intell. Res.,2002

3. Associative learning on imbalanced environments: an empirical study;Cleofas-Sánchez;Expert Syst. Appl.,2016

4. Support vector networks;Corres;Mach. Learn.,1995

5. Wavelet neural network-based fault diagnosis in air-handling units;Du;Hvac. & R. Res.,2008

Cited by 34 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advance and prospect of machine learning based fault detection and diagnosis in air conditioning systems;Renewable and Sustainable Energy Reviews;2024-11

2. A new chiller fault diagnosis method under the imbalanced data environment via combining an improved generative adversarial network with an enhanced deep extreme learning machine;Engineering Applications of Artificial Intelligence;2024-11

3. An Improved Graph Deviation Network for Chiller Fault Diagnosis by Integrating the Sparse Cointegration Analysis and the Convolutional Block Attention Mechanism;Energies;2024-08-13

4. Fault detection and diagnosis in light commercial buildings’ HVAC systems: A comprehensive framework, application, and performance evaluation;Energy and Buildings;2024-08

5. An interpretable graph convolutional neural network based fault diagnosis method for building energy systems;Building Simulation;2024-06-20

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3