Fault detection in ventilation units using dynamic energy performance models

Mattera, Claudio Giovanni; Jradi, Muhyiddine; Skydt, Mathis Riber; Engelsgaard, Sebastian Skals; Shaker, Hamid Reza

doi:10.1016/j.jobe.2020.101635

Cited by 7 publications

(8 citation statements)

References 13 publications

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“…Even though these This review's main dividers (model-based and data-based) were further split into qualitative and quantitative methods. Both model-based and data-based qualitative methods focus on rules [47][48][49] and relations between parameters [8,50,51]. Contrarily, the modelbased quantitative methods focus more on using a reference model to compare the measured data from the system [42,43,52].…”

Section: Methods Categorizations For Fddmentioning

confidence: 99%

“…On the one hand, model-based methods have the advantage of typically being based on a system's physics, thereby enabling easier understanding and interpretation of the behavior and results of the methods [49,72,73]. On the other hand, they have the disadvantage of needing experts to set them up for each system individually, meaning that it the process to create them can be labor-intensive initially.…”

Section: Model-based Methodsmentioning

confidence: 99%

“…Otherwise, there was a risk of an increase in the false-positive results. For one-step FDD, SVM was used 18 times [56,57,70,92-106], with 9 of these regarding CCS, while rulesets were used 4 times for AHU [48,49,113,114]. Typically, labeled datasets are needed for one-step FDD, as supervised-learning algorithms are used.…”

Section: Algorithm Distributionmentioning

confidence: 99%

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Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

et al. 2022

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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.

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Section: Methods Categorizations For Fddmentioning

confidence: 99%

Section: Model-based Methodsmentioning

confidence: 99%

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Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

et al. 2022

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“…However, major questions were raised regarding the scalability and generalization of the results. Moreover, multiple studies recently have highlighted failures in BACS operation, improper controls and management techniques, lack of automation in operation as major causes of building energy performance gaps, leading to a reduction in the overall building energy efficiency [35,36]. Therefore, it was highlighted that a special consideration should be given not only to the design of BACS but also its operation, which needs to be considered to be a major component in buildings initial and retro-commissioning processes.…”

Section: Building Automation System Auditing and Evaluationmentioning

confidence: 99%

AUSTRET: An Automated Step Response Testing Tool for Building Automation and Control Systems

Santos

Jradi

2021

Energies

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Building energy consumption is still one of the main contributions to global carbon emissions. With the overall digitalization in the building sector, building automation and control systems (BACS) are to play a more important and key role in improving the building sector performance. A well-designed BACS at the building design phase with a high level of control functionalities is not a guarantee for efficient building operation and successful control and management strategies in the operational phase. Thus, a systematic automated initial and retro-commissioning process is key to test the performance of the automation system and the response of the integrated HVAC systems. This is an arduous and time-consuming task susceptible to human errors. As an alternative, the current study proposes a methodological framework to automate step response testing of BACS and to optimize the different steps of this process in a cost-effective way. In addition to newly built buildings, the framework can be applied in existing or retrofitted medium to large-sized buildings that have a building management system capable of receiving actuator commands and responsible to provide updates of several state variables. Based on the proposed framework, a first-of-its kind tool “AUSTRET” for building automated step response testing of BACS is designed and developed. The tool provides the necessary input configuring parameters, building system selection, and output results for each performed test. The framework aims to act upon ventilation, room heating and cooling, and water heating and cooling modules in a building. The implementation and demonstration of the AUSTRET in a medium-sized building case study for two different building systems are presented and evaluated: (1) Ventilation/fan, (2) Room heating. The results show the different dynamic responses on these two systems and how misleading input parameter configuration can invalidate step response tests. The preliminary results highlight the capability of using AUSTRET as a key component in both building initial and retro-commissioning applications.

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“…Precise diagnostics of system defects can help the system become fault-tolerant and self-healing in the future [8]. Fault detection and diagnosis (FDD) methods in HVAC systems are classified in a variety of ways, according to the literature study [7,9], such as data driven-based approaches [10,11] and knowledge-based strategies [12][13][14][15]. However, to improve accuracy and overcome the limitations associated with this method, some researchers employed a combination of different approaches [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

User-Centric BIM-Based Framework for HVAC Root-Cause Detection

Alavi

Forcada

2022

Energies

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In the building operation phase, the Heating, Ventilation, and Air-Conditioning (HVAC) equipment are the main contributors to excessive energy consumption unless proper design and maintenance is carried out. Moreover, HVAC problems might have an impact on occupants’ discomfort in thermal comfort. Hence, the identification of the root cause of HVAC problems is imperative for facility managers to plan preventive and corrective maintenance actions. However, due to the complex interaction between various equipment and the lack of data integration among Facility Management (FM) systems, they fail to provide necessary information to identify the root cause of HVAC problems. Building Information Modelling (BIM) is a potential solution for maintenance activities to address the challenges of information reliability and interoperability. Therefore, this paper presents a novel conceptual model and user-centric framework to determine the causes of HVAC problems implemented in BIM for its visualization. CMMS and BMS data were integrated into BIM and utilized by the framework to analyze the root cause of HVAC problems. A case study in a university building was used to demonstrate the applicability of the approach. This framework assists the FM team to determine the most probable cause of an HVAC problem, reducing the time to detect equipment faults, and providing potential actions to solve them.

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Fault detection in ventilation units using dynamic energy performance models

Cited by 7 publications

References 13 publications

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

AUSTRET: An Automated Step Response Testing Tool for Building Automation and Control Systems

User-Centric BIM-Based Framework for HVAC Root-Cause Detection

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