Academic literature on the topic 'Diagnosis of the building'

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Journal articles on the topic "Diagnosis of the building"

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Mohammed, Bukar, Zanna Ali, Ibrahim Ahmed, and Mohammed Adamu. "BUILDING CONDITION SURVEY AND SYSTEMATIC DEFECTS DIAGNOSIS ON LIBRARY BUILDING IN NIGERIA." International Journal of Innovative Research in Education, Technology & Social Strategies 8, no. 1 (March 25, 2021): 76–84. http://dx.doi.org/10.48028/iiprds/ijiretss.v8.i1.08.

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The building under investigation is at Mai Idris Alooma Polytechnic Geidam library situated in Yobe State North Eastern, Nigeria. The building is being occupied by large number of people many defects propelled were associated with cracks, discoloration, settlement, dampness, peeling, moisture and mold grow on the buildings besides environmental factors. These compelled the researcher to conduct the investigation to look into possible remedies in line with the bulk stage defects diagnostic that will be useful in identifying the aspect. The information gathered through survey of the affected building under investigation based on spatial severity degree of damages suffered on the buildings. Moreover; the analysis for the investigation is based on Assessment Rating system (BARIS) that provide very high-resolution to process the interferometric techniques. However, after a critical survey of the building facades from each side the researcher explores the major defects found on the building that is mostly attributed due to poor workmanship, general climatic conditions, and practical inadequacy during construction. Therefore, the diagnosis will predict the overall judgment for the type of repair required.
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Yuan, Shuang, Zhen-Zhong Hu, Jia-Rui Lin, and Yun-Yi Zhang. "A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data." Sensors 21, no. 4 (February 17, 2021): 1395. http://dx.doi.org/10.3390/s21041395.

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Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China.
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Al-Makhadmee, Zafer. "Diagnosis System for Building Management Network." Research Journal of Applied Sciences, Engineering and Technology 6, no. 23 (December 15, 2013): 4451–54. http://dx.doi.org/10.19026/rjaset.6.3450.

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Lo, Tommy Y., and K. T. W. Choi. "Building defects diagnosis by infrared thermography." Structural Survey 22, no. 5 (December 2004): 259–63. http://dx.doi.org/10.1108/02630800410571571.

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Bernard, Rosanne St, and Cyrus C. Hsia. "Building bridges to diagnosis and malignancy." Blood 124, no. 11 (September 11, 2014): 1843. http://dx.doi.org/10.1182/blood-2014-06-581983.

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Wang, Yanwei, Hanyuan Zhang, Guiqing Zhang, Feng Tian, and Fei Ren. "Application of Improved Apriori Algorithm in Diagnosis of Abnormal Building Energy Consumption." E3S Web of Conferences 257 (2021): 02060. http://dx.doi.org/10.1051/e3sconf/202125702060.

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The analysis of building abnormal energy consumption is of great significance to the effective energy saving of buildings. To apply the relationship between the running status of building equipment and energy consumption to the diagnosis of abnormal energy consumption, an abnormal diagnosis method of building energy consumption based on the improved Apriori association rules is proposed. An improved Apriori algorithm is proposed for building energy consumption data with a large amount of data and multi-value attributes. The improved Apriori algorithm determines whether different attribute values of the same attribute data are in advance when generating candidate sets, reduces the number of comparisons, and improves the algorithm efficiency. By analyzing the abnormal energy consumption of the chiller in the refrigeration station of a commercial building, the superiority of the improved Apriori algorithm is proved, and the abnormal energy consumption is found, which verifies the feasibility and practicability of the proposed method.
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Balado, J., L. Díaz-Vilariño, P. Arias, and M. Soilán. "Automatic building accessibility diagnosis from point clouds." Automation in Construction 82 (October 2017): 103–11. http://dx.doi.org/10.1016/j.autcon.2017.06.026.

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Watts, Brook, Pushkar Argekar, Sanjay Saint, and Carol A. Kauffman. "Building a Diagnosis from the Ground Up." New England Journal of Medicine 356, no. 14 (April 5, 2007): 1456–62. http://dx.doi.org/10.1056/nejmcps055322.

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Pratiwi, S. N., P. Wijayanto, and C. A. Putri. "Diagnosis of capillary rise in heritage building." IOP Conference Series: Earth and Environmental Science 780, no. 1 (May 1, 2021): 012076. http://dx.doi.org/10.1088/1755-1315/780/1/012076.

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Zhou, Lu, Guang-geng Li, Yu-mei Zhou, Dan Yin, Yan Sun, Yan Zheng, and Yu-hang Li. "Traditional Chinese Medicine (TCM) Diagnosis Model Building Based on Multi-label Classification." MATEC Web of Conferences 232 (2018): 02026. http://dx.doi.org/10.1051/matecconf/201823202026.

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In the study, we propose a TCM diagnosis model that can be used for multi-label classification and give clear diagnosis, as well as the basis for diagnosis and differentiation when the symptoms correspond to multiple diseases or syndromes. The implementation of the model is divided into three steps. Firstly, choose the machine learning algorithm to train the TCM diagnosis model. The features of the training data are symptoms and the labels are diseases or syndromes. Secondly, give the number α (α>1, α∈Z+), the model will output the diagnoses with the top α highest probability according to the input symptoms as candidate diagnoses. Finally, the rules of differential diagnosis are designed to determine which candidate diagnoses should be reserved, thereby complete the multi-label classification. In our test dataset, by 10-fold cross-validation, the average accuracy of the single label classification was 0.882; the average precision was 0.974; the average recall was 1.000; the average f1 score was 0.967; the average accuracy of the multi-label classification was 0.706; the average micro precision was 0.934; the average micro recall was 0.941 and the average hamming loss was 0.060. Through the test we can know that this model had a good potential for auxiliary decision making in clinical diagnosis and treatment.
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Dissertations / Theses on the topic "Diagnosis of the building"

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Zhong, Binglin. "Model building and machine fault diagnosis." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340889.

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Shi, Hongsen. "Building Energy Efficiency Improvement and Thermal Comfort Diagnosis." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555110595177379.

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Yang, Yingying. "Innovative non-destructive methodology for energy diagnosis of building envelope." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0913/document.

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Le secteur du bâtiment représente 35% des la consommations énergétiques dans les pays membres de l’agence international de l’énergie en 2010 et 39,8% aux Etats-Unis en 2015. Plus de 50% de cette consommation a été utilisée pour la production de chaleur et de froid. Néanmoins cette consommation peut être réduite par l'amélioration la performance énergétique du bâtiment. La performance thermique de l'enveloppe du bâtiment joue un rôle primordial. Par conséquent, le diagnostic thermique de l'enveloppe du bâtiment est nécessaire pour, par exemple, la réception de nouvelles constructions, l'amélioration de la performance énergétique des anciens bâtiments, ainsi que la vente et la location des logements. Pourtant, il existe très peu de méthodes quantitatives pour la caractérisation des parois épaisses. L'objectif de cette étude est d'explorer des méthodes quantitatives innovantes de diagnostic thermique de l'enveloppe du bâtiment. Des mesures expérimentales ont été réalisées en laboratoire (à l’IFSTTAR à Nantes) et in situ (à l’IUT de Bordeaux). Différents capteurs et méthodes d'instrumentation ont été étudiés pour mesurer la densité de flux et la température de surfaces des parois, afin de procurer des recommandations pour le choix des capteurs ainsi que des protocoles de traitement de données. A partir des données mesurées (température et densité de flux des surfaces de l'enveloppe), trois approches numériques ont été proposées pour estimer des paramètres thermiques des parois multicouches épaisses : par méthode inverse, par réponse à un échelon et par réponse impulsionnelle. En outre, une méthode innovante non-destructive utilisant la rayonnement térahertz a été étudiée. Les mesures ont été effectuées au sein du laboratoire I2M. Cette méthode permet de caractériser le coefficient d'absorption des matériaux constructifs ordinaires comme isolation, plâtre, béton, bois… Elle pourrait postérieurement être combinée avec une méthode thermique pour apporter des informations complémentaires
Buildings represent a large share in terms of energy consumption, such as 35% in the member countries of IEA (2010) and 39.8% in U.S. (2015). Climate controlling (space heating and space cooling) occupies more than half of the consumption. While this consumption can be reduced by improving the building energy efficiency, in which the thermal performance of building envelope plays a critical role. Therefore, the thermal diagnosis of building envelope is of great important, for example, in the case of new building accreditation, retrofitting energy efficiency of old building and the building resale and renting. However, very few diagnostic methods exist for the characterization of thick walls. The present measurement standards that based on steady state heat transfer regime need a long time (several days). The classical transient technologies, such as flash method, are difficult to implement on the walls because of the large thickness of walls and the complex conditions in situ. This thesis aims to explore innovative methodologies for thermal quantitative diagnosis of building envelope. Two experimental cases were carried out: one is in laboratory (IFSTTAR, Nantes) and the other is in situ (IUT, Bordeaux). Different sensors and instruments were studied to measure the wall heat flux and surface temperature, and provided some guidelines for the choice of sensors and data processing protocols as well. Using these measured data, three estimation approaches were proposed to estimate the thermal parameters of the multilayer thick wall: pulse response curve method, step response curve method and inverse method, which can be applied for different diagnostic situations. In addition, an innovative NDE (non-destructive evaluation) method using terahertz (THz) radiation was also investigated. Measurements were carried out in I2M laboratory to characterize the absorption coefficient of standard building materials (insulation, plaster, concrete, wood ...). This THz method can be combined with a previous thermal method to provide some complementary information
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Guan, Qiang. "Autonomic Failure Identification and Diagnosis for Building Dependable Cloud Computing Systems." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc499993/.

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The increasingly popular cloud-computing paradigm provides on-demand access to computing and storage with the appearance of unlimited resources. Users are given access to a variety of data and software utilities to manage their work. Users rent virtual resources and pay for only what they use. In spite of the many benefits that cloud computing promises, the lack of dependability in shared virtualized infrastructures is a major obstacle for its wider adoption, especially for mission-critical applications. Virtualization and multi-tenancy increase system complexity and dynamicity. They introduce new sources of failure degrading the dependability of cloud computing systems. To assure cloud dependability, in my dissertation research, I develop autonomic failure identification and diagnosis techniques that are crucial for understanding emergent, cloud-wide phenomena and self-managing resource burdens for cloud availability and productivity enhancement. We study the runtime cloud performance data collected from a cloud test-bed and by using traces from production cloud systems. We define cloud signatures including those metrics that are most relevant to failure instances. We exploit profiled cloud performance data in both time and frequency domain to identify anomalous cloud behaviors and leverage cloud metric subspace analysis to automate the diagnosis of observed failures. We implement a prototype of the anomaly identification system and conduct the experiments in an on-campus cloud computing test-bed and by using the Google datacenter traces. Our experimental results show that our proposed anomaly detection mechanism can achieve 93% detection sensitivity while keeping the false positive rate as low as 6.1% and outperform other tested anomaly detection schemes. In addition, the anomaly detector adapts itself by recursively learning from these newly verified detection results to refine future detection.
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De, Waard Simon Rudi. "Fault identification and diagnosis for telephone exchange building facilities / S.R. de Waard." Thesis, North-West University, 2005. http://hdl.handle.net/10394/1058.

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Heating, ventilating and air conditioning (HVAC) systems consume 43 % of the energy used by buildings. This percentage grows when the HVAC system operates with malfunctions. Fault detection and diagnosis (FDD) methods are developed to reduce abnormal events and down-times and to promote energy saving use of equipment. Most FDD methodologies for HVAC systems found in the literature revolve around first principle models and mathematical models. This dissertation describes a FDD solution based on process history data and artificial neural network (ANN) models. ANN models, of HVAC components, are built from fault-free operation data. Faulty data are then used with the ANN models to build various residuals and statistical residual transformations. From these residuals, unique residual patterns are assigned to discern between a variety of malfunctions. This FDD strategy is, firstly, applied to a static pressure control loop and secondly, applied to the overall power consumption of an HVAC system. In both studies, the FDD system successfully detected and classified unwanted anomalies - some deviating as little as 5% from normal operational standards. Finally, the FDD system is rated according to a common set of criteria reviewed in the literature study. This criterion shows the FDD strategy to be robust and adaptable, with low modelling and computational requirements.
Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2005.
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Gao, Tianyun. "Integrated building fault detection and diagnosis using data modeling and Bayesian networks." Thesis, Ecole nationale supérieure Mines-Télécom Lille Douai, 2020. http://www.theses.fr/2020MTLD0010.

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Dans les systèmes de chauffage, de ventilation et de climatisation (CVC, en anglais HVAC pour « Heating, Ventilation and Air Conditioning »), les défauts des équipements et les erreurs de fonctionnement causent des problèmes de confort et un gaspillage d’énergie. Pour aider les gestionnaires d’installations à identifier et à corriger plus efficacement les défauts, il est essentiel de disposer d’un outil de détection et de diagnostic automatique de défauts (AFDD), capable de détecter automatiquement les problèmes de confort et d’énergie et d’en identifier les causes.Les méthodes AFDD existantes se concentrent principalement sur la détection et le diagnostic de défauts au niveau des équipements. Peu d’attention n’est accordée au diagnostic au niveau du bâtiment global, qui permet une détection plus efficace en s’appuyant sur l’interdépendance entre les équipements tout au long de la chaîne de distribution d’énergie. Cette thèse propose une nouvelle méthode AFDD pour les bâtiments, basée sur les données d’exploitation collectées par les systèmes de gestion technique du bâtiment (GTB, en anglais BMS pour « Building Management System »). Cette méthode est conçue autour d’un réseau bayésien qui permet de détecter les défauts des équipements HVAC en s’appuyant sur une meilleure décision basée sur la fusion des informations et des données provenant des différents niveaux de composants. Cela permet de réaliser un diagnostic de défaut intégré de la globalité du bâtiment. Une originalité importante de cette contribution porte sur l’exploitation des historiques de fonctionnement et des techniques d’apprentissage pour aider au paramétrage automatique de l’outil de détection. Notre méthodologie comprend les deux parties suivantes :1. Une nouvelle manière systématique de transférer des informations de topologie de systèmes de bâtiment et des connaissances d’experts pour la construction modulaire du réseau bayésien.2. Une nouvelle approche pour intégrer des détections de défaut au niveau des équipements dans un réseau bayésien de diagnostic du bâtiment complet. Nous utilisons une méthode de régression pour les équipements centraux (par exemple, groupe froid, chaudière et central traitement d’air), apprise sur des données de fonctionnement normal collectées lors d’un test de mise en service. Pour les équipements dans les zones d’usage, nous utilisons un modèle probabiliste des corrélations entre données de consigne et de mesure.Une fois le réseau mis en place et les données – mesures et prédictions – collectées, le réseau est à même de calculer la probabilité de différents défauts dans le système bâtiment complet, et d’en identifier les causes les plus probables.Nous avons testé ce nouvel outil de diagnostic des défauts sur des données provenant de simulation et de deux bâtiments réels afin de tester les performances en termes de détection. Les résultats montrent que notre approche est capable de gérer facilement un grand nombre d’équipements et d’identifier correctement les causes à partir des données mesurées et prédites au niveau des équipements.Par rapport aux approches de type AFDD existantes, cette nouvelle méthode offre les avantages suivants :1) La structure modulaire et la méthodologie généralisée permettent à cette méthode d’être appliquée à une grande variété de systèmes CVC et de bâtiments. 2) Cette approche relie les défauts d’équipement aux symptômes de confort du bâtiment perceptibles par les occupants.3) Le système HVAC est diagnostiqué dans son ensemble au lieu de le faire équipement par équipement.4) En connectant chaque violation de point de consigne de confort avec les défauts des équipements, et en recherchant les défauts racines pour chaque défaut d’équipement de zone, le nombre total d’alarmes est grandement réduit.5) Les gestionnaires d’installations peuvent utiliser l’outil de manière interactive, en mettant à jour les données de certains noeuds du réseau bayésien sur la base d’observations terrain
Heating, ventilation, and air-conditioning (HVAC) equipment faults and operational errors result in comfort issues and waste of energy in buildings. In order to help the facility managers to identify and fix faults more efficiently, it is essential to have an Automatic Fault Detection and Diagnosis (AFDD) tool, able to automatically detect comfort and energy issues and identify the root faults.Existing AFDD methods mostly focus on equipment-level fault detection and diagnostics. Almost no attention is given to building level fault diagnosis, considering inter-dependency between equipment through the energy distribution chain.This thesis proposes a new building AFDD method based on operation data collected by Building Management Systems (BMS). The method uses Bayesian Network to achieve building-level integrated fault diagnosis and equipment-level data-driven fault detection by information fusion of data collected from different equipment of HVAC systems. An important contribution relates to the use of operating data and learning techniques to automatically tune some parameters of the detection tool.Our methodology is composed of the following two parts:1. A new systematic way of transferring building system topology information and expert knowledge to a Bayesian Network.2. A novel approach for integrating equipment-level fault detection results into a building-level fault diagnosis Bayesian network. We use regression methods for central equipment (e.g. chiller, boiler, and Air Handling Unit), learned from normal operation data collected during a commissioning test. For room equipment, we use probabilistic models of correlations between control and measurement data.Once the fault diagnosis network is set up and all of the evidence is collected, the network is able to calculate the probability of different faults and identify the most probable root faults. We implemented the fault diagnostics Bayesian network on one simulation data set and two real building operation data sets to test the performance of the AFDD method. The results show that the method is able to easily handle large numbers of equipment, and correctly identify root causes with given evidences.Compared to existing AFDD methods, the new method provides the following benefits:1) The modular structure and generalized methodology allow the method to be applied to wide variety of HVAC systems.2) The method connects equipment faults to building comfort symptoms perceivable by occupants.3) The HVAC system is diagnosed as a whole instead of equipment by equipment.4) By connecting comfort set point violation with equipment fault, and tracing root fault of room equipment failure, the total number of alarms is reduced.5) Facility managers can use the tool in an interactive way, thanks to the capability to post evidence in the Bayesian network based on field investigation findings
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Djuric, Natasa. "Real-time supervision of building HVAC system performance." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Engineering Science and Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-2215.

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This thesis presents techniques for improving building HVAC system performance in existing buildings generated using simulation-based tools and real data. Therefore, one of the aims has been to research the needs and possibilities to assess and improve building HVAC system performance. In addition, this thesis aims at an advanced utilization of building energy management system (BEMS) and the provision of useful information to building operators using simulation tools.

Buildings are becoming more complex systems with many elements, while BEMS provide many data about the building systems. There are, however, many faults and issues in building performance, but there are legislative and cost-benefit forces induced by energy savings. Therefore, both BEMS and the computer-based tools have to be utilized more efficiently to improve building performance.

The thesis consists of four main parts that can be read separately. The first part explains the term commissioning and the commissioning tool work principal based on literature reviews. The second part presents practical experiences and issues introduced through the work on this study. The third part deals with the computer-based tools application in design and operation. This part is divided into two chapters. The first deals with improvement in the design, and the second deals with the improvement in the control strategies. The last part of the thesis gives several rules for fault diagnosis developed using simulation tools. In addition, this part aims at the practical explanation of the faults in the building HVAC systems.

The practical background for the thesis was obtained though two surveys. The first survey was carried out with the aim to find the commissioning targets in Norwegian building facilities. In that way, an overview of the most typical buildings, HVAC equipment, and their related problems was obtained. An on-site survey was carried out on an example building, which was beneficial for introducing the building maintenance structure and the real hydronic heating system faults.

Coupled simulation and optimization programs (EnergyPlus and GenOpt) were utilized for improving the building performances. These tools were used for improving the design and the control strategies in the HVAC systems. Buildings with a hydronic heating system were analyzed for the purpose of improving the design. Since there are issues in using the optimization tool, GenOpt, a few procedures for different practical problems have been suggested. The optimization results show that the choice of the optimization functions influences significantly the design parameters for the hydronic heating system.

Since building construction and equipment characteristics are changing over time, there is a need to find new control strategies which can meet the actual building demand. This problem has been also elaborated on by using EnergyPlus and GenOpt. The control strategies in two different HVAC systems were analyzed, including the hydronic heating system and the ventilation system with the recovery wheel. The developed approach for the strategy optimization includes: involving the optimization variables and the objective function and developing information flow for handling the optimization process.

The real data obtained from BEMS and the additional measurements have been utilized to explain faults in the hydronic heating system. To couple real data and the simple heat balance model, the procedure for the model calibration by use of an optimization algorithm has been developed. Using this model, three operating faults in the hydronic heating system have been elaborated.

Using the simulation tools EnergyPlus and TRNSYS, several fault detection and diagnosis (FDD) rules have been generated. The FDD rules were established in three steps: testing different faults, calculating the performance indices (PI), and classifying the observed PIs. These rules have been established for the air cooling system and the hydronic heating system. The rules can diagnose the control and the component faults. Finally, analyzing the causes and the effects of the tested faults, useful information for the building maintenance has been descriptively explained.

The most important conclusions are related to a practical connection of the real data and simulation-based tools. For a complete understanding of system faults, it is necessary to provide real-life information. Even though BEMS provides many building data, it was proven that BEMS is not completely utilized. Therefore, the control strategies can always be improved and tuned to the actual building demands using the simulation and optimization tools. It was proven that many different FDD rules for HVAC systems can be generated using the simulation tools. Therefore, these FDD rules can be used as manual instructions for the building operators or as a framework for the automated FDD algorithms.


Denne avhandlingen presenterer noen fremgangsmåter for forbedring av ytelser for VVS-tekniske anlegg i eksisterende bygninger basert på bruk av simuleringsverktøy og virkelige måledata. Ett av målene har vært å undersøke behov og muligheter for vurdering og forbedring av ytelser for VVS-anlegg i bygninger. I tillegg har denne avhandlingen hatt som mål å fremme bruk av SD-anlegg samt å fremskaffe nyttig informasjon til driftspersonalet.

Bygninger blir stadig mer kompliserte systemer som inneholder flere og flere komponenter mens SD-anlegg håndterer en stadig større mengde data fra bygningsinstallasjoner. På den ene siden registreres det ofte feil og problemer med hensyn til ytelsene til de VVS-tekniske installasjonene. På den andre siden innføres det stadig strengere lovmessige pålegg og kost-nyttekrav motivert i energieffektiviseringen. SD-anlegg og databaserte verktøy bør derfor brukes mer effektivt for forbedring av ytelsene.

Avhandlingen består av fire hoveddeler hvor hver del kan leses separat. Den første delen, som er basert på literatturstudie, forklarer funksjonskontroll som begrep og prinsipper for oppbygging av verktøy for funksjonskontroll. Den andre delen presenterer praktisk erfaring og problemstillinger utviklet og behandlet i løpet av arbeidet med avhandlingen. Den tredje delen handler om anvendelse av databaserte verktøy for forbedring av ytelsen for VVS-tekniske installasjoner. Den tredje delen er delt opp i to kapitler, hvorav et handler om forbedring av systemløsninger og et om forbedring av styringsstrategier. Den siste delen presenterer flere regler for feilsøking og diagnostisering utviklet gjennom bruk av simuleringsverktøy. I tillegg gir denne delen en praktisk forklaring av feilene i de VVS-anleggene som er behandlet i undersøkelsen.

Det praktiske grunnlaget for avhandlingen er etablert gjennom to undersøkelser. Den første var en spørreundersøkelse som hadde til hensikt å kartlegge målsetninger for funksjonskontroll i norske bygninger. Gjennom dette ble det etablert en oversikt over de mest typiske bygninger med tilhørende VVS-anlegg og de mest forekommende problemene. En dypere undersøkelse ble utført på ett casebygg. Denne undersøkelsen viste seg å være nyttig både for kartlegging av betydningen av organisering av driften av bygningen og for avdekking av de virkelige feilene i det vannbårne oppvarmingssystemet.

En kobling mellom et simulerings- og et optimaliseringsprogram (EnergyPlus og GenOpt) har vært benyttet for forbedring av ytelsene for de VVS-tekniske installasjonene. Disse verktøyene har vært brukt for forbedring av både systemløsningene og styringsstrategiene for VVS-anlegg. Bygninger med vannbåren oppvarmingssystem har vært analysert for å forbedre systemløsningen. På grunn av begrensninger i bruken av optimaliseringsverktøyet GenOpt, har det blitt utviklet egne prosedyrer for håndtering av visse typer problemstillinger hvor denne begrensningen opptrer. Resultatene for optimaliseringen viser at valg av objektfunksjoner påvirker betydelig parametrene i det vannbårne oppvarmingssystemet.

Endringer i egenskapene til både bygningskonstruksjoner og utstyr som skjer på grunn av aldring over tiden, gjør det nødvendig med tilpassning av styringsstrategier slik at det virkelige behovet kan bli dekket. Denne problemstillingen har vært analysert ved bruk av EnergyPlus og GenOpt. Styringsstrategiene for to forskjellige VVS-anlegg, et vannbåret oppvarmingssystem og et ventilasjonsanlegg med varmegjenvinner har blitt behandlet. Den utviklete prosedyren for optimalisering av styringsstrategien består av følgende steg: innføring av optimaliseringsvariabler og objektfunksjon, samt utvikling av informasjonsflyt for behandling av optimaliseringsprosessen.

De virkelige data, både fra SD-anlegg og tilleggsmålinger, har vært benyttet for praktisk forklaring av feilene i oppvarmingssystemet. En prosedyre for modellkalibrering basert på bruk av en optimaliseringsalgoritme som kobler sammen de virkelige data og en enkel varmebalansemodell har blitt foreslått. Tre konkrete driftsfeil i oppvarmingssystemet har blitt belyst gjennom bruk av denne varmebalansemodellen.

Flere regler for feilsøking og diagnostisering har blitt utviklet ved hjelp av simuleringsverktøyene EnergyPlus and TRNSYS. Denne utviklingen har bestått av tre ulike steg: testing av bestemte feil, beregning av ytelsesindikatorer og til slutt klassifisering av de observerte ytelsesindikatorer. Reglene har blitt utviklet for et system av aggregater for luftkjøling og for et vannbåret oppvarmingssystem. Reglene kan diagnostisere både styringsfeil og komponentfeil. Til slutt presenteres informasjon som er nyttig for drift av VVS-tekniske installasjoner i bygninger basert på en analyse av årsakene for og virkningene av de feil som er behandlet.

De viktigste konklusjonene er knyttet til praktisk kombinasjon av virkelige måleverdier og simuleringsverktøy. Informasjon fra det virkelig liv er helt nødvendig for å få en god forståelse av feil som oppstår i anlegg. Det er også vist at potensialet som ligger i alle de data som er tilgjengelige gjennom SD-anlegg, ikke er fullt utnyttet. Gjennom bruk av simuleringsverktøy kan styringsstrategiene alltid bli bedre tilpasset og innjustert til de virkelige behov i bygningen. Simuleringsverktøy kan også brukes for utvikling av prosedyrer for feilsøking og diagnostisering i VVS-tekniske anlegg. Disse prosedyrene kan brukes enten som en veileder for manuell feilsøking og detektering eller som grunnlag for utvikling av automatiserte algoritmer.


Paper II, VI and VII are reprinted with kind permission from Elsevier, sciencedirect.com
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Materano, Antonella. "The building blocks of social entrepreneurship: empirical model and framework." Master's thesis, NSBE - UNL, 2013. http://hdl.handle.net/10362/11631.

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics
The purpose of this exploratory study is to identify a common path followed by social entrepreneurs, so as to build a comprehensive empirical model. The methodology used is qualitative interviews; in particular, semi-structured questions were addressed to a sample of ten social entrepreneurs, whose answers were transcribed and analysed. The main result is represented by a five-stage pattern followed by social entrepreneurs: each stage is firstly described and further linked to specific challenges that social entrepreneurs face and assets they need during the process. It is fundamental to highlight that some of these stages and challenges are peculiar to social entrepreneurship, differing from regular entrepreneurship. The key conclusion is that it is possible to identify a common pattern that could guide current and future social entrepreneurs. Furthermore, this research paper emphasises best practices and lesson learned from current social entrepreneurs by leaving a powerful heritage to who is interested in make a real change in society.
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Patil, N. "In-situ measurement methods for characterisation and diagnosis of airborne sound transmission through multi-layered building partitions." Thesis, University of Salford, 2018. http://usir.salford.ac.uk/46321/.

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The thesis concerns with the development of novel measurement methods for characterisation and diagnosis of airborne sound transmission through building partitions. Using standard tests, the airborne sound insulation of partitions can be measured as the Sound Reduction Index ‘SRI’. While the SRI provides the frequency dependence of sound insulation (or transmission), the local/spatial sound transmission through various paths in the partition is not known. If the contributions of different paths in the partition can be measured then any weak paths of sound insulation can be diagnosed. This would be especially useful in the case of multi-layered partitions where the sound insulation depends on the sound transmission through point connections/ribs/studs/frame, etc. present in the structure. While different theoretical models are in place to predict the sound insulation in presence of such elements, the experimental diagnosis of their sound transfer contributions remains fairly unexplored. Similar diagnosis problems are encountered by automotive industry while dealing with structure borne sources in the vehicle. In practice, Transfer Path Analysis (TPA) methods are extensively used in such cases for diagnosing the contributions of different structure borne sources at vehicle interiors. Application of such TPA methods for diagnosing airborne sound transmission is challenging on various counts. Firstly, the airborne source applies a continuous excitation on the receiver as opposed to structure borne sources which are typically discrete. Secondly, for our study, the path contributions are desired which is difficult than measuring source characterisations. To address these issues, a novel TPA application Inverse-Airborne Source Contribution Analysis (I-ASCA) is devised which employs a patch based discretisation of the source receiver interface for the diagnosis of airborne sound transmission through partitions. Using such discretisation, the airborne excitation on the partition can be inversely characterised by blocked forces and the source contributions can be measured. Additionally, a new methodology Inverse Path Contribution Analysis (I-PCA) is outlined which allows for measurement of path contributions. These methodologies applied to the case of single and double layer partitions excited by airborne source and the accuracy of the methods was found to be within 2-3 dB of the measured response in general up to a maximum of 1 kHz under the tested grid size. The accuracy of the method is thus strictly linked to the discretisation size. A sampling criterion of λ_b/2 was found to be sufficient which is less demanding than sampling criterion utilised by finite element methods. The methods can be applied to >1 kHz range if the discretisation can be made finer. To improve the practical application of the methods, the Direct-Airborne Source Contribution Analysis (D-ASCA) is presented which allows for direct characterisation of the airborne excitation using contact pressures. The method is much faster than I-ASCA in providing source contributions however path contributions cannot be measured using this method and the accuracy of the method is also found to be within 2-3 dB. D-ASCA application has been presented for the case of commercial single and double casement windows. Using careful assumptions, it is possible to estimate the path contributions of the glazing and frame in the windows from the source contributions. The diagnosis allows comparing the path contributions in frequency regions up to 1 kHz and the weak path is identified. This shows the potential of the method in identifying the weak elements of sound insulation which can be used as a complement to the SRI data and can provide cues for improving the sound insulation of the partition. Lastly, an in-situ measurement method for airborne sound insulation measurement is presented which can be applied when a pressure doubling occurs at the surface. A novel approach has been presented to assess whether pressure doubling occurs on the surface and calculating the blocked pressures. This allows one to measure SRI in-situ using diagnostic measurements without the need of a separate standard test for measuring SRI. This showcases the versatility of the approaches in that the frequency dependence (SRI) and spatial dependence (path contributions) of sound insulation/transmission can be measured within a single approach.
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Alshehri, Abdullah Mohammed. "Conflict in architectural projects : diagnosis and avoidance : a study based on Saudi Arabian construction industry." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/conflict-in-architectural-projects--diagnosis-and-avoidancea-study-based-on-saudi-arabian-construction-industry(795de607-2709-4c24-9373-7762ce303c0a).html.

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The main purpose of this research project was to bridge the existing knowledge gap inthe empirical identification and understanding of how conflict occurs between keyproject parties within Saudi Arabia’s public sector building projects. Such conflict hasbecome an increasingly endemic feature within the last 20 years, and this researchproject provides a contribution in knowledge terms which will help to overcome theobstacles and challenges impeding growth and development in the field. This wasachieved by conducting an investigation to provide the theoretical background aboutthe antecedents of conflict, and presenting a number of project management suggestions to avoid or minimise.Both qualitative and quantitative research approaches were utilised in this study. Thequalitative research data was obtained from 30 in-depth semi-structured interviewswith four types of key project party, namely, project owners, consultants, contractors,and sub-contractors. This was followed by two separate questionnaire surveys. The firstwas a means of validating conflict data obtained from the interviews, and the secondwas used to test Project Management - PM data, . In this part of the study, 672questionnaires were sent to various people engaged in the Saudi Arabian constructionindustry. The response rate was 46.1% (n = 310 ).In terms of the interview data, a total of 349 data items were derived and from these data items, 30 general themes emerged concerning various causes of conflict and the latent conditions of conflict, providing descriptions of what and how conflicts arise within Saudi Arabian public building projects. From these general themes, 31 recommendations for strategic project management processes are made, with the intention of preventing or at least minimising conflict. The quantitative survey conducted to test these project management strategies (recommendations) revealed that all of them were supported. The study subsequently produced a cyclical framework of conflict avoidance, derived from the research methodology used in the study, and this is outlined to enable project building participants, whether individuals, groups, or organisations, to improve their project management strategy from project to project.The research recommends that: generally, certain project management strategiesshould be implemented in the earlier phases of a project in order to promote conflictavoidance behaviours or at least to effect a reduction in these. Furthermore, strategicactions are required to deal with the latent condition-related issues in respect of building projects in the Saudi Arabian context. In this case, reforms to current practices are required to improve the performance within the building industry. It is also recommended that further research be undertaken to explore other latent conditions of conflict and conflicts themselves in order to develop additional project management strategies aimed at managing the causes of conflict.
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Books on the topic "Diagnosis of the building"

1

Building failures: Diagnosis and avoidance. 2nd ed. London: E. & F.N. Spon, 1987.

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Delgado, João M. P. Q., ed. Recent Developments in Building Diagnosis Techniques. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0466-7.

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European Geosciences Union. General Assembly. Building stone decay: From diagnosis to conservation. London: Geological Society, 2007.

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Rougier, Philip A. The diagnosis of building defects by computer. Ascot, Berks: Chartered Institute of Building, 1994.

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Addleson, Lyall. Building failures: A guide to diagnosis, remedy, and prevention. 2nd ed. London: Butterworth Architecture, 1989.

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Building failures: A guide to diagnosis, remedy, and prevention. 3rd ed. Oxford: Butterworth Architecture, 1992.

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Sick buildings: Definition, diagnosis, and mitigation. Boca Raton: Lewis Publishers, 1995.

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Godart, Bruno. Guide to Diagnosis and Appraisal of AAR Damage to Concrete in Structures: Part 1 Diagnosis (AAR 6.1). Dordrecht: Springer Netherlands, 2013.

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E, Press Christopher, ed. Building market strength through DRGs. Chicago, Ill: Pluribus Press, 1986.

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Ballast, David Kent. Building diagnostics. Monticello, Ill., USA: Vance Bibliographies, 1987.

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Book chapters on the topic "Diagnosis of the building"

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Son, Lee How, and George C. S. Yuen. "Diagnosis and Investigation Techniques." In Building Maintenance Technology, 29–43. London: Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-23150-8_3.

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Kwan, A. K. H., and P. L. Ng. "Building Diagnostic Techniques and Building Diagnosis: The Way Forward." In Lecture Notes in Mechanical Engineering, 849–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09507-3_74.

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Emeritus, Satoshi Ishikawa, PhD, Professor, and Mikio Miyata, PhD, MD, Professor Emeritus. "5 Diagnosis." In Chemical Sensitivity and Sick-Building Syndrome, 29–36. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9781315374451-6.

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Son, Lee How, and George C. S. Yuen. "Concrete Defects — Inspection and Diagnosis." In Building Maintenance Technology, 100–123. London: Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-23150-8_6.

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Annila, Petri J., Jukka Lahdensivu, Jommi Suonketo, and Matti Pentti. "Practical Experiences from Several Moisture Performance Assessments." In Recent Developments in Building Diagnosis Techniques, 1–20. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0466-7_1.

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Erba, Silvia, Bruno Daniotti, Elisabetta Rosina, Antonio Sansonetti, and Riccardo Paolini. "Evaluation of Moisture Transfer to Improve the Conservation of Tiles Finishing Facades." In Recent Developments in Building Diagnosis Techniques, 171–94. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0466-7_10.

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Guerra, Fernanda Lamego, Eduardo Grala da Cunha, and Fábio Galli. "Analysis of Microclimate in a Historical Building to Assess the Probability of Recurrence of Filamentous Fungi." In Recent Developments in Building Diagnosis Techniques, 195–213. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0466-7_11.

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Stolz, Carina Mariane, and Angela Borges Masuero. "Analysis of Main Parameters that Affect Contact Area Between Mortar Rendering and Substrate: Use of 3D Laser Scanning Technique." In Recent Developments in Building Diagnosis Techniques, 21–35. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0466-7_2.

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Lembo, Filiberto, and Francesco Paolo R. Marino. "The Pathologies of the ETICS." In Recent Developments in Building Diagnosis Techniques, 37–49. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0466-7_3.

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Bauer, Elton, Elier Pavón, Cláudio H. F. Pereira, and Matheus L. M. Nascimento. "Criteria for Identification of Ceramic Detachments in Building Facades with Infrared Thermography." In Recent Developments in Building Diagnosis Techniques, 51–68. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0466-7_4.

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Conference papers on the topic "Diagnosis of the building"

1

Romessis, C., and K. Mathioudakis. "Bayesian Network Approach for Gas Path Fault Diagnosis." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53801.

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A method for solving the gas path analysis problem of jet engine diagnostics based on a probabilistic approach is presented. The method is materialized through the use of a Bayesian Belief Network (BBN). Building a BBN for gas turbine performance fault diagnosis requires information of a stochastic nature expressing the probability of whether a series of events occurred or not. This information can be extracted by a deterministic model and does not depend on hard to find flight data of different faulty operations of the engine. The diagnostic problem and the overall diagnostic procedure are first described. A detailed description of the way the diagnostic procedure is set-up, with focus on building the BBN from an engine performance model, follows. The case of a turbofan engine is used to evaluate the effectiveness of the method. Several simulated and benchmark fault case scenarios have been considered for this reason. The examined cases demonstrate that the proposed BBN-based diagnostic method composes a powerful tool. This work also shows that building a diagnostic tool, based on information provided by an engine performance model, is feasible and can be efficient as well.
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Sharma, Seema, and Deepti Mehrotra. "Building CBR based diagnosis system using jCOLIBRI." In 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence (Confluence). IEEE, 2017. http://dx.doi.org/10.1109/confluence.2017.7943229.

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Beasley, Kimball J. "Identification and Diagnosis of Building Façade Failures." In Fifth Forensic Engineering Congress. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41082(362)8.

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Dai, Yinglong, Guojun Wang, Sihong Chen, and Xiaofei Xing. "Building deep architectures for traditional chinese medicine diagnosis." In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2017. http://dx.doi.org/10.1109/uic-atc.2017.8397497.

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Douangnoulack, Phonethep, and Veera Boonjing. "Building Minimal Classification Rules for Breast Cancer Diagnosis." In 2018 10th International Conference on Knowledge and Smart Technology (KST). IEEE, 2018. http://dx.doi.org/10.1109/kst.2018.8426198.

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Gatton, Thomas M., and Frank W. Kearney. "Automated Knowledge Acquistion for Building Diagnosis Expert Systems." In 6th International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 1989. http://dx.doi.org/10.22260/isarc1989/0073.

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Palacios, Luis, Gaëlle Lortal, Claire Laudy, Christian Sannino, Ludovic Simon, Giuseppe Fusco, Yue Ma, and Chantal Reynaud. "Avionics Maintenance Ontology Building for Failure Diagnosis Support." In 8th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0006092002040209.

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Zhang, Xuedian, Min Chang, Ma Yiu Man, Wu Kin Mo, and Yeung Chi Hang. "A PDA-Based Intelligent Building Service System." In 2009 IEEE Circuits and Systems International Conference on Testing and Diagnosis. IEEE, 2009. http://dx.doi.org/10.1109/cas-ictd.2009.4960765.

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Wang, Dianhui. "Data Mining for Building Rule-based Fault Diagnosis Systems." In 2006 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.280946.

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Najeh, Houda, Mahendra Pratap Singh, Karim Chabir, Stephane Ploix, and Mohamed Naceur Abdelkrim. "Fault Detection and Diagnosis for Building Systems: New Challenges." In 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2018. http://dx.doi.org/10.1109/ssd.2018.8570606.

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Reports on the topic "Diagnosis of the building"

1

Wen, Jin, and Yimin Chen. VOLTTRON COMPATIBLE WHOLE BUILDING ROOTFAULT DETECTION AND DIAGNOSIS. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1524639.

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Frank, Stephen M., Guanjing Lin, Xin Jin, Rupam Singla, Amanda Farthing, Liang Zhang, and Jessica Granderson. Metrics and Methods to Assess Building Fault Detection and Diagnosis Tools. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1503166.

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Bae, Yeonjin, Borui Cui, Jaewan Joe, Piljae Im, Veonica Adetola, Liang Zhang, Matt Leach, and Teja Kuruganti. Review: Sensor Impact on Building Controls and Automatic Fault Detection and Diagnosis (AFDD). Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1671427.

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Im, Piljae, Yeonjin Bae, Borui Cui, Seungjae Lee, Saptarshi Bhattacharya, Veronica Adetola, Draguna Vrabie, Liang Zhang, and Matt Leach. Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD). Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1649168.

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Sebald, A., and M. A. Piette. Diagnostics for building commissioning and operation. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/672145.

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Katipamula, S., and N. Gayeski. Building Diagnostic Market Deployment - Final Report. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1041315.

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Katipamula, Srinivas, and N. Gayeski. Building Diagnostic Market Deployment - Final Report. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1041371.

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Judkoff, R., and J. Neymark. International Energy Agency building energy simulation test (BESTEST) and diagnostic method. Office of Scientific and Technical Information (OSTI), February 1995. http://dx.doi.org/10.2172/90674.

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Thomas, Gail F., Susan P. Hocevar, and Erik Jansen. A Diagnostic Approach to Building Collaborative Capacity in an Interagency Context. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada458540.

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Neymark, J., R. Judkoff, D. Alexander, C. Felsmann, P. Strachan, and A. Wijsman. International Energy Agency Building Energy Simulation Test and Diagnostic Method (IEA BESTEST) Multi-Zone Non-Airflow In-Depth Diagnostic Cases: MZ320 -- MZ360. Office of Scientific and Technical Information (OSTI), September 2008. http://dx.doi.org/10.2172/938998.

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