Special Issue "Fault Identification and Fault Impact Analysis of Ventilation System in Buildings"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Energy and Buildings".

Deadline for manuscript submissions: 1 December 2021.

Special Issue Editors

Prof. Alireza Afshari
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Guest Editor
Department of the Built Environment, Aalborg University, Copenhagen, Denmark
Interests: ventilation; indoor climate; energy; district heating; energy storage; control; building
Special Issues and Collections in MDPI journals
Dr. Jan Bendtsen
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Guest Editor
Department of Electronic Systems, Aalborg University, Aalborg, Denmark
Interests: automation; control; energy; building; fault detection
Dr. Samira Rahnama
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Guest Editor
Department of the Built Environment, Aalborg University, Copenhagen, Denmark
Interests: controllers; model predictive control, smart grid, energy storage; indoor climate; ventilation; energy; control; building

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to fault modeling, fault detection and diagnostics (FDD), and fault impact analysis (FIA) with focus on heating, ventilation and air conditioning (HVAC) systems. Buildings use 40% of total global energy and are responsible for more than 35% of CO2 emissions. In most buildings, the heating, ventilation and air conditioning (HVAC) systems consume 50% of the building energy. Access to information on the actual energy performance of buildings and its systems is essential in order to improve energy efficiency, leading to considerable reduction in GHG emissions and end-user costs. Today’s energy performance calculation of buildings is at the design stage, which does not account for the dynamic variation of the energy performance over time. The inefficient use of energy in buildings, for instance, the inefficient energy use of common faulty systems, is a question that spans the whole process of building planning, design, construction, operation and maintenance.

The HVAC systems are a priority since they are the largest end-use energy consumption in buildings. Furthermore, these systems are well known to be highly inefficient and could represent a 5–20% annual energy saving if failures are detected and fixed. HVAC system inefficiencies have several root causes such as design problems, malfunctioning and/or unnoticed faults in one of the parts of the system— valves, coils, fans, boilers, and pumps. Oversized components and bad design of the control system are very common causes of energy waste. In both cases, even if the system is working as designed, the energy is not efficiently used. On the other hand, malfunctioning components and unnoticed faults cause energy waste during the periods that such problems remain unaddressed. This period can be very long since a well-designed control system compensates the fault and, consequently, there is no perceptible change in the environmental conditions of the served space.

Prof. Alireza Afshari
Dr. Jan Bendtsen
Dr. Samira Rahnama
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fault modeling
  • fault detection and diagnostics
  • fault impact analysis
  • HVAC
  • indoor climate
  • energy

Published Papers (1 paper)

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Research

Article
Fault Diagnosis of DCV and Heating Systems Based on Causal Relation in Fuzzy Bayesian Belief Networks Using Relation Direction Probabilities
Energies 2021, 14(20), 6607; https://doi.org/10.3390/en14206607 - 13 Oct 2021
Viewed by 251
Abstract
The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts with a high capacity in the reasoning of uncertainties, diagnostics of different [...] Read more.
The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts with a high capacity in the reasoning of uncertainties, diagnostics of different fault severities, and understandability. However, these methods involve higher and more time-consuming effort; they require a deep understanding of the causal relationships between faults and symptoms; and there is still a lack of automatic approaches to improving the efficiency. The data-driven methods rely on similarities and patterns, and they are very sensitive to changes of patterns and have more accuracy than the knowledge-driven methods, but they require massive data for training, cannot inform about the reason behind the result, and represent black boxes with low understandability. The research problem is thus the combination of knowledge-driven and data-driven diagnosis in DCV and heating systems, to benefit from both categories. The diagnostic method presented in this paper involves less effort for experts without requiring deep understanding of the causal relationships between faults and symptoms compared to existing knowledge-driven methods, while offering high understandability and high accuracy. The fault diagnosis uses a data-driven classifier in combination with knowledge-driven inference with both fuzzy logic and a Bayesian Belief Network (BBN). In offline mode, for each fault class, a Relation-Direction Probability (RDP) table is computed and stored in a fault library. In online mode, we determine the similarities between the actual RDP and the offline precomputed RDPs. The combination of BBN and fuzzy logic in our introduced method analyzes the dependencies of the signals using Mutual Information (MI) theory. The results show the performance of the combined classifier is comparable to the data-driven method while maintaining the strengths of the knowledge-driven methods. Full article
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