Automation-assisted fault detection of an air-handling unit; implementing the method in a real building

Pakanen, Jouko; Sundquist, Tero

doi:10.1016/s0378-7788(02)00050-6

Cited by 24 publications

(4 citation statements)

References 11 publications

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“…Usually it is difficult to find the examples of real faults, so in the work of J.E. Pakanen et al [14], a few artificial faults are introduced for on-line diagnostic tests of AHU. All the faults in our study are found on the case study building.…”

Section: Introductionmentioning

confidence: 99%

Heating system performance estimation using optimization tool and BEMS data

Djuric

Novaković

Frydenlund

2008

Energy and Buildings

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Causes and effects of a few real faults in a hydronic heating system are explained in this paper. Since building energy management system (BEMS) has to be utilized in fault detection and diagnosis (FDD), practical explanations of faults and their related effects are important to building caretakers. A simple heat balance model is used in this study. The model is calibrated using the optimization tool. Site data from the BEMS of a real building are calibrated against the model. Desired and real data are compared, so that the effects of the following faults are analyzed: faults in an outdoor air temperature sensor, fault in the time schedule, and a water flow imbalance problem. This paper presents an overview of the real causes of the faults and their effects both on the energy consumption and the indoor air temperature. In addition, simple instructions for the building caretakers for fault detection in the hydronic heating systems are given.

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Section: Introductionmentioning

confidence: 99%

Heating system performance estimation using optimization tool and BEMS data

Djuric

Novaković

Frydenlund

2008

Energy and Buildings

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“…A plenty of researchers have explored various techniques to analyze the large amount of data to optimize building energy use, such as using ML to predict energy usage and identify patterns in data. Furthermore, big data analytics have also been applied to identify faults in building systems for secure and reliable operation [60,61].…”

Section: Big Data and Building Automationmentioning

confidence: 99%

Advanced controls on energy reliability, flexibility and occupant-centric control for smart and energy-efficient buildings

Liu

Zhang

Sun

et al. 2023

Energy and Buildings

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“…Most of the fault diagnosis methods in the literature rely on pure statistical approaches such as artificial neural networks, support vector machines, principle component analysis and wavelet transform, cumulative sums analysis, auto-regressive models with exogenous inputs. Only a few studies [38,42,50,53,60,70,87,100] attempted to characterize the BAS data by using the underlying physical processes through greybox modelling. Predominantly, the diagnoses of faults in the reviewed literature [70] were undertaken via a passive approach in which control signals were not generated for commissioning pu poses.…”

Section: Faults Listed Inmentioning

confidence: 99%

Characterization of a building's operation using automation data: A review and case study

Gunay

Shen

Yang

2017

Building and Environment

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Vous avez des questions?Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca.Abstract: This paper presents a critical review of the automated on-going commissioning (AOGC) methods for air-handling units (AHU) and variable air volume terminal (VAV) units in commercial buildings. The common faults studied in the literature were identified. The diagnostic approaches taken and the characteristics of the fault-symptom datasets utilized were categorized. It was found that the diagnostics methods were vastly fragmented, and most of them employed pure-statistical approaches. Only a few studies attempted to assimilate the automation data within the underlying physical processes. In addition, a large fraction of the reviewed literature has been devoted to physical faults in AHUs. Only a few studies were conducted to diagnose faults-related with controls programming and faults at the zone level. Upon the literature survey findings, an inverse greybox modelling-based AOGC approach was put forward. Its strengths and weaknesses were demonstrated through a case study conducted using the archived building automation system (BAS) data of an office building in Ottawa, Canada. The results of this case study indicate that inverse greybox modelling-based AOGC is a promising method to diagnose both physical and controls programming related faults at AHUs and VAVs.

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Automation-assisted fault detection of an air-handling unit; implementing the method in a real building

Cited by 24 publications

References 11 publications

Heating system performance estimation using optimization tool and BEMS data

Heating system performance estimation using optimization tool and BEMS data

Advanced controls on energy reliability, flexibility and occupant-centric control for smart and energy-efficient buildings

Characterization of a building's operation using automation data: A review and case study

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