Unsupervised domain adaptation for HVAC fault diagnosis using contrastive adaptation network

IF 6.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Naghmeh Ghalamsiah , Jin Wen , K.Selcuk Candan , Teresa Wu , Zheng O’Neill , Asra Aghaei
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引用次数: 0

Abstract

Data-driven methods have shown great promise for heating, ventilation, and air conditioning (HVAC) systems’ fault diagnosis, but their reliance on well-labeled datasets poses challenges in real-world applications where such data may not be readily available. Meanwhile, well-labeled data might exist from virtual testbeds or laboratory systems. Domain adaptation could provide a solution to utilize labeled data from a source domain (such as a virtual or laboratory testbed) to diagnose faults in an unlabeled target domain, such as faults in a real building system. This paper utilizes the contrastive adaptation network (CAN) algorithm, originally successful in image classification, to overcome the specific challenges faced by current domain adaptation algorithms in HVAC systems. Furthermore, temporal causal discovery framework (TCDF), a causality-based framework for discovering causal relationships in time series data, is implemented in the data processing step to meet the requirements of convolutional networks, where spatially closer features are more likely to be correlated. The results on air handling unit (AHU) datasets demonstrate that the CAN algorithm effectively facilitates domain adaptation in the absence of target labels and that the feature reordering process reduces the training time and the number of loops required for convergence.

Abstract Image

基于对比自适应网络的无监督域自适应暖通空调故障诊断
数据驱动的方法在供暖、通风和空调(HVAC)系统的故障诊断方面显示出巨大的前景,但它们对标记良好的数据集的依赖在现实应用中构成了挑战,因为这些数据可能不容易获得。同时,标记良好的数据可能存在于虚拟试验台或实验室系统中。领域自适应可以提供一种解决方案,利用来自源领域(例如虚拟或实验室测试平台)的标记数据来诊断未标记目标领域中的故障,例如真实建筑系统中的故障。本文利用对比自适应网络(对比自适应网络,CAN)算法克服了当前领域自适应算法在暖通空调系统中所面临的具体挑战,该算法最初在图像分类方面取得了成功。此外,在数据处理步骤中实现了时序因果发现框架(TCDF),这是一种基于因果关系的框架,用于发现时间序列数据中的因果关系,以满足卷积网络的要求,其中空间上更接近的特征更有可能相互关联。在空气处理机组(AHU)数据集上的结果表明,CAN算法在没有目标标签的情况下有效地促进了域适应,并且特征重新排序过程减少了收敛所需的训练时间和环路数量。
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来源期刊
Energy and Buildings
Energy and Buildings 工程技术-工程:土木
CiteScore
12.70
自引率
11.90%
发文量
863
审稿时长
38 days
期刊介绍: An international journal devoted to investigations of energy use and efficiency in buildings Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.
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