Journal article
An entropy-based causality framework for cross-level faults diagnosis and isolation in building HVAC systems
Energy and buildings, v 317, 114378
15 Aug 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
•Entropy-based causality learning framework is proposed for building fault diagnosis.•The proposed synchronicity concept is employed to measure building symptom correlations.•Causal learning aims to determine Bayesian network structure.•Modelica test cases demonstrate the framework’s effectiveness.
Faults, such as malfunctioning sensors, equipment, and control systems, significantly affect a building’s performance. Automatic fault detection and diagnosis (AFDD) tools have shown great potential in improving building performances, including both energy efficiency and indoor environment quality. Since modern buildings have integrated systems where multiple subsystems and equipment are coupled, many faults in a building are cross-level faults, i.e., faults occurring in one component that trigger operational abnormalities in other subsystems. Compared with non-cross-level faults, it is more challenging to isolate the root cause of a cross-level faults due to the system coupling effects. Bayesian networks (BNs) have been studied for the root cause isolation for building faults. While promising, existing BN-based diagnosis methods highly rely on expert domain knowledge, which is time-consuming and labor expensive, especially for cross-level faults. To address this challenge, we propose an entropy-based causality learning framework, termed Eigen-Entropy Causal Learning (EECL), to learn BN structures. The proposed method is data-driven without the use of expert domain knowledge; it utilizes causal inference to determine the causal mechanisms between faults status and symptoms to construct the BN model. To demonstrate the effectiveness of the proposed framework, three fault test cases are used for evaluation in this study. Experimental results show that the BN constructed by the proposed framework is able to conduct building cross-level faults diagnosis with a comparable isolation accuracy to those by domain knowledge while maintaining less complexed BN structure.
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Details
- Title
- An entropy-based causality framework for cross-level faults diagnosis and isolation in building HVAC systems
- Creators
- Jiajing Huang - Arizona State UniversityNaghmeh Ghalamsiah - Drexel UniversityAbhidnya Patharkar - Advanced Imaging ResearchOjas Pradhan - Drexel UniversityMengyuan Chu - Texas A&M UniversityTeresa Wu - Advanced Imaging ResearchJin Wen - Drexel UniversityZheng O’Neill - Texas A&M UniversityKasim Selcuk Candan - Arizona State University
- Publication Details
- Energy and buildings, v 317, 114378
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:001256789400001
- Scopus ID
- 2-s2.0-85195406677
- Other Identifier
- 991021960804704721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Construction & Building Technology
- Energy & Fuels
- Engineering, Civil