The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
Computers are promising tools for providing educational experiences that meet individual learning needs. However, delivering this promise in practice is challenging, particularly when automated feedback is essential and the learning extends beyond using traditional methods such as writing and solving mathematics problems. We hypothesize that interactive knowledge representations can be deployed to address this challenge. Knowledge representations differ markedly from concept maps. Where the latter uses nodes (concepts) and arcs (links between concepts), a knowledge representation is based on an ontology that facilitates automated reasoning. By adjusting this reasoning towards interacting with learners for the benefit of learning, a new class of educational instruments emerges. In this contribution, we present three projects that use an interactive knowledge representation as their foundation. DynaLearn supports learners in acquiring system thinking skills. Minds-On helps learners to deepen their understanding of phenomena while performing experiments. Interactive Concept Cartoons engage learners in a science-based discussion about controversial topics. Each of these approaches has been developed iteratively in collaboration with teachers and tested in real classrooms, resulting in a suite of lessons available online. Evaluation studies involving pre-/post-tests and action-log data show that learners are easily capable of working with these educational instruments and that the instruments thus enable a semi-automated approach to constructive learning.
This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Het project FIXAR richt zich op het beantwoorden van de vraag: Hoe kan de luchtvaart- en windenergiesector composietenreparaties middels geautomatiseerde technologieën economisch verantwoord maken? Deze vraag komt voort uit eerdere ervaringen in RAAK-mkb projecten op het gebied van composietfabricage, oriëntatie op de nationale en internationale markt en uit de feedback van het betrokken mkb. Het mkb staat voor de uitdaging kennis en ervaring met automatiseringsoplossingen op te doen en nieuwe inspectietechnologieën in te voeren, wil het de groeiende behoefte aan composietenreparaties het hoofd bieden. De doelstelling van het project is dan ook, het door praktijkgericht onderzoek ontwikkelen van geautomatiseerde methoden voor duurzame geautomatiseerde composietenreparaties die technisch- en economisch haalbaar zijn. Om dit doel te bereiken wordt door Hogeschool Inholland samengewerkt met een aantal kennisinstituten en mkb-partners. Het project is opgebouwd rondom vier deelonderzoeken. Hiermee zijn alle aspecten van composietenreparaties gedekt; hulpmiddelen voor geautomatiseerde reparaties, inspectie en validatie, materiaalonderzoek en opleiding van medewerkers. Gelet op de state of the art-kennis, ligt de focus op luchtvaart en windenergie. Het zijn namelijk juist deze twee sectoren die het meest van elkaar kunnen profiteren. Binnen de deelonderzoeken komen state of the art-zaken aan bod als drones en Augement Reality. Aangezien het onderzoek zich richt op actuele problemen bij de bedrijven, zal een deel van het onderzoek bij de bedrijven zelf plaatsvinden en kunnen deze bedrijven direct profiteren van de resultaten van het onderzoek. In het onderwijs komen stage- en afstudeerplekken beschikbaar voor de studenten van de deelnemende hogescholen. Daarnaast vindt er een duurzame vertaalslag plaats van de projectresultaten en bevindingen middels het realiseren van onderwijsmateriaal t.b.v. de curricula van de opleidingen aviation, luchtvaarttechnologie, werktuigbouwkunde, en technische informatica. Het project heeft een blijvende impact op de beroepspraktijk omdat het deelnemende mkb met de resultaten uit dit project hun kennis van reparatieprocessen op hoger niveau brengt.
Het analyseren van grote gegevensbestanden om de kwaliteit van het onderwijs te verbeteren is een hot item. De toepassing van learning analytics kan het onderwijs verbeteren. Wij doen onderzoek naar learning analytics en de vaardigheden die gebruikers daarbij nodig hebben.Doel Wij onderzoeken wat de gevolgen zijn van databewerking op de uitkomsten van learning analytics. En welke vaardigheden hebben gebruikers nodig om deze systemen zinvol te gebruiken? Learning analytics Learning analytics is het meten, verzamelen, analyseren en rapporteren van data van studenten en hun omgeving om het leren en de leeromgeving te begrijpen en te verbeteren. Het gebruik van learning analyticssystemen Het realiseren van grote delen van de onderwijsvisie van Hogeschool Utrecht is sterk verbonden met de succesvolle uitvoering van analyses op studentniveau. Het gebruik van learning analyticssystemen is niet vanzelfsprekend. De ontwerpers en ontwikkelaars van deze systemen moeten helder zijn over hun ontwerpkeuzes (zoals manieren van databewerking en de werking van algoritmes). Anderzijds moeten studenten en docenten beschikken over datavaardigheden om deze systemen op een zinvolle manier te gebruiken. Resultaten Dit onderzoek loopt. Na afloop vind je hier een samenvatting van de resultaten. In juli 2019 verscheen het volgende artikel van de onderzoekers: Automated Feedback for Workplace Learning in Higher Education. Looptijd 01 september 2017 - 31 december 2020 Aanpak We hebben eerst verkennend onderzoek gedaan door een case study waarin onderzocht is wat de effecten zijn van verschillende keuzes in de data cleaning op de uitkomsten van de data-analyse. Vanaf september 2019 gaan we onderzoeken welke datavaardigheden studenten nodig hebben om learning analytics-systemen effectief te gebruiken.
