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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 "G: Energy and Buildings".

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 16642

Special Issue Editors

Prof. Dr. Alireza Afshari

E-Mail Website
Guest Editor
Department of the Built Environment, Aalborg University, 9100 Copenhagen, Denmark
Interests: ventilation; indoor climate; energy; district heating; energy storage; control; building
Special Issues, Collections and Topics in MDPI journals
Dr. Jan Bendtsen

E-Mail Website
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

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Keywords

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

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Published Papers (5 papers)

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Research

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21 pages, 3151 KiB  
Article
Fault Identification and Fault Impact Analysis of The Vapor Compression Refrigeration Systems in Buildings: A System Reliability Approach
by Mostafa Fadaeefath Abadi, Mohammad Hosseini Rahdar, Fuzhan Nasiri and Fariborz Haghighat
Energies 2022, 15(16), 5774; https://doi.org/10.3390/en15165774 - 9 Aug 2022
Cited by 2 | Viewed by 2771
Abstract
The Vapor Compression Refrigeration System (VCRS) is one of the most critical systems in buildings typically used in Heating, Ventilation, and Air Conditioning (HVAC) systems in residential and industrial sections. Therefore, identifying their faults and evaluating their reliability are essential to ensure the [...] Read more.
The Vapor Compression Refrigeration System (VCRS) is one of the most critical systems in buildings typically used in Heating, Ventilation, and Air Conditioning (HVAC) systems in residential and industrial sections. Therefore, identifying their faults and evaluating their reliability are essential to ensure the required operations and performance in these systems. Various components and subsystems are included in the VCRS, which need to be analyzed for system reliability. This research’s objective is conducting a comprehensive system reliability analysis on the VCRS by focusing on fault identification and determining the fault impacts on these systems. A typical VCRS in an office building is selected for this research regarding this objective. The corresponding reliability data, including the probability distributions and parameters, are collected from references to perform the reliability evaluation on the components and subsystems of the VCRS. Then the optimum distribution parameters have been obtained in the next step as the main findings. Additionally, by applying optimization techniques, efforts have been taken to maximize the system’s reliability. Finally, a comparison between the primary and the optimized systems (with new distribution parameters) has been performed over their lifetime to illustrate the system’s improvement percentage. Full article
(This article belongs to the Special Issue Fault Identification and Fault Impact Analysis of Ventilation System in Buildings)
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26 pages, 15395 KiB  
Article
Realistic Simulation of Sensor/Actuator Faults for a Dependability Evaluation of Demand-Controlled Ventilation and Heating Systems
by Bahareh Kiamanesh, Ali Behravan and Roman Obermaisser
Energies 2022, 15(8), 2878; https://doi.org/10.3390/en15082878 - 14 Apr 2022
Cited by 4 | Viewed by 1789
Abstract
In the development of fault-tolerant systems, simulation is a common technique used to obtain insights into performance and dependability because it saves time and avoids the risks of testing the behavior of real-world systems in the presence of faults. Fault injection in a [...] Read more.
In the development of fault-tolerant systems, simulation is a common technique used to obtain insights into performance and dependability because it saves time and avoids the risks of testing the behavior of real-world systems in the presence of faults. Fault injection in a simulation offers a high controllability and observability, and thus is ideal for an early dependability analysis and fault-tolerance evaluation. Heating, ventilation, and air conditioning (HVAC) systems in critical infrastructures, such as airports and hospitals, are safety-relevant systems, which not only determine energy consumption, system efficiency, and occupancy comfort but also play an essential role in emergency scenarios (e.g., fires, biological hazards). Hence, fault injection serves as a practical and essential solution to assess dependability in different fault scenarios of HVAC systems. Hence, in this paper, we present a simulation-based fault injection framework with a combination of two techniques, simulator command and simulation code modification, which are applied to fault injector blocks as saboteurs and an automated fault injector algorithm to automatically activate fault cases with certain fault attributes. The proposed fault injection framework supports a comprehensive range of faults and various fault attributes, including fault persistence, fault type, fault location, fault duration, and fault interarrival time. This framework considers noise in a demand-controlled ventilation (DCV) and heating system as a type of HVAC system since it has been demonstrated that any fault injection scenario is accompanied by some impacts on energy consumption, occupancy comfort, and a fire risk. It also supports the reproducibility for a set of specific fault scenarios or random fault injection scenarios. The system model was implemented and simulated in Matlab/Simulink, and fault injector blocks were developed by Stateflow diagrams. An experimental evaluation serves as the assessment of the presented fault injection framework with a defined example of fault scenarios. The results of the evaluation show the correctness, system behavior, accuracy, and other parameters of the system, such as the heater energy consumption and heater duty cycle of the fault injection framework in the presence of different fault cases. In conclusion, the present paper provides a novel simulation-based fault injection framework, which combines simulator command techniques and simulation code modifications for a realistic and automatic fault injection with comprehensive coverage of various fault types and a consideration of noise and uncertainty, allowing for reproducibility of the results. The outputs achieved from the fault injection framework can be applied to fault-tolerant studies in other application domains. Full article
(This article belongs to the Special Issue Fault Identification and Fault Impact Analysis of Ventilation System in Buildings)
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47 pages, 50185 KiB  
Article
Fault Diagnosis of DCV and Heating Systems Based on Causal Relation in Fuzzy Bayesian Belief Networks Using Relation Direction Probabilities
by Ali Behravan, Bahareh Kiamanesh and Roman Obermaisser
Energies 2021, 14(20), 6607; https://doi.org/10.3390/en14206607 - 13 Oct 2021
Cited by 6 | Viewed by 2107
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
(This article belongs to the Special Issue Fault Identification and Fault Impact Analysis of Ventilation System in Buildings)
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Review

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16 pages, 862 KiB  
Review
Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review
by Amir Rafati, Hamid Reza Shaker and Saman Ghahghahzadeh
Energies 2022, 15(1), 341; https://doi.org/10.3390/en15010341 - 4 Jan 2022
Cited by 37 | Viewed by 4486
Abstract
Heat, ventilation, and air conditioning (HVAC) systems are some of the most energy-intensive equipment in buildings and their faulty or inefficient operation can significantly increase energy waste. Non-Intrusive Load Monitoring (NILM), which is a software-based tool, has been a popular research area over [...] Read more.
Heat, ventilation, and air conditioning (HVAC) systems are some of the most energy-intensive equipment in buildings and their faulty or inefficient operation can significantly increase energy waste. Non-Intrusive Load Monitoring (NILM), which is a software-based tool, has been a popular research area over the last few decades. NILM can play an important role in providing future energy efficiency feedback and developing fault detection and diagnosis (FDD) tools in smart buildings. Therefore, the review of NILM-based methods for FDD and the energy efficiency (EE) assessment of HVACs can be beneficial for users as well as buildings and facilities operators. To the best of the authors’ knowledge, this paper is the first review paper on the application of NILM techniques in these areas and highlights their effectiveness and limitations. This review shows that even though NILM could be successfully implemented for FDD and the EE evaluation of HVACs, and enhance the performance of these techniques, there are many research opportunities to improve or develop NILM-based FDD methods to deal with real-world challenges. These challenges and future research works are also discussed in-depth. Full article
(This article belongs to the Special Issue Fault Identification and Fault Impact Analysis of Ventilation System in Buildings)
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Other

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50 pages, 7536 KiB  
Systematic Review
Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review
by Simon P. Melgaard, Kamilla H. Andersen, Anna Marszal-Pomianowska, Rasmus L. Jensen and Per K. Heiselberg
Energies 2022, 15(12), 4366; https://doi.org/10.3390/en15124366 - 15 Jun 2022
Cited by 20 | Viewed by 4348 | Correction
Abstract
This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: [...] Read more.
This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository. Full article
(This article belongs to the Special Issue Fault Identification and Fault Impact Analysis of Ventilation System in Buildings)
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