Novel transformer-based self-supervised learning methods for improved HVAC fault diagnosis performance with limited labeled data

计算机科学 暖通空调 机器学习 人工智能 监督学习 变压器 学习迁移 半监督学习 人工神经网络 数据挖掘 工程类 空调 机械工程 电气工程 电压
作者
Cheng Fan,Yutian Lei,Yongjun Sun,Like Mo
出处
期刊:Energy [Elsevier]
卷期号:278: 127972-127972 被引量:3
标识
DOI:10.1016/j.energy.2023.127972
摘要

Existing data-driven HVAC fault diagnosis methods mainly adopt supervised learning paradigms, making them less feasible/implementable for individual buildings with limited labeled data. Considering the demanding requirements of domain expertise and labor work associated in data labeling, advanced data analytics are urgently needed to utilize massive unlabeled operational data for reliable predictive modeling. Therefore, this study proposes a novel transformer-based self-supervised learning methodology for improved HVAC fault diagnosis performance using limited labeled data. Three self-supervised learning approaches are developed to extract knowledge from unlabeled operational data through self-prediction and contrastive learning tasks. A customized transformer-based neural network is designed to ensure the efficiency and effectiveness in tabular data analysis and knowledge transfer. Data experiments have been conducted using multiple HVAC datasets considering different data availabilities, self-supervised learning approaches and model architectures. The results validate the capabilities of self-supervised learning in developing reliable HVAC fault classification models. Compared with conventional supervised learning solutions, the methodology proposed not only substantially reduce the data labelling works required, but also improves the fault diagnosis performance by up to 8.44%. The research outcomes are valuable for upgrading predictive modeling protocols in the building field for developing easy-implementation and high-performance data-driven solutions with limited labeled data.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
12rcli完成签到,获得积分10
1秒前
fuchao发布了新的文献求助10
2秒前
3秒前
6秒前
6秒前
酷炫小懒虫完成签到,获得积分10
8秒前
dd发布了新的文献求助10
10秒前
111完成签到 ,获得积分10
11秒前
微光包子完成签到,获得积分10
11秒前
JL完成签到 ,获得积分10
11秒前
肆4完成签到 ,获得积分10
12秒前
13秒前
gzmejiji完成签到 ,获得积分10
13秒前
聚乙二醇完成签到 ,获得积分10
13秒前
南枝完成签到,获得积分10
19秒前
20秒前
今后应助ww采纳,获得10
21秒前
共享精神应助bukeshuo采纳,获得10
22秒前
贾贾爱科研完成签到,获得积分10
23秒前
小叶发布了新的文献求助10
24秒前
努力加油煤老八完成签到 ,获得积分10
24秒前
hh发布了新的文献求助10
24秒前
meiyang完成签到 ,获得积分10
24秒前
24秒前
April完成签到 ,获得积分10
25秒前
NexusExplorer应助高兴断秋采纳,获得10
25秒前
26秒前
小陈爱涂六神完成签到 ,获得积分10
28秒前
28秒前
羊白玉发布了新的文献求助20
29秒前
陶醉的大白完成签到 ,获得积分10
32秒前
大方谷梦完成签到 ,获得积分10
33秒前
34秒前
所所应助哈哈哈666采纳,获得10
35秒前
35秒前
穿堂风发布了新的文献求助10
36秒前
小叶完成签到,获得积分10
36秒前
慕青应助郝宝真采纳,获得10
39秒前
Anny完成签到 ,获得积分10
40秒前
高分求助中
Evolution 10000
ISSN 2159-8274 EISSN 2159-8290 1000
Becoming: An Introduction to Jung's Concept of Individuation 600
Ore genesis in the Zambian Copperbelt with particular reference to the northern sector of the Chambishi basin 500
A new species of Coccus (Homoptera: Coccoidea) from Malawi 500
A new species of Velataspis (Hemiptera Coccoidea Diaspididae) from tea in Assam 500
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3162896
求助须知:如何正确求助?哪些是违规求助? 2813938
关于积分的说明 7902359
捐赠科研通 2473525
什么是DOI,文献DOI怎么找? 1316888
科研通“疑难数据库(出版商)”最低求助积分说明 631545
版权声明 602187