CC-BY-NC-ND This paper was presented at the IADIS Multi Conference on Computer Science and Information Systems MCCSIS2020 There is an increasing interest in indoor occupation and guidance information for business and societal purposes. Scientific literature has paid attention to various ways of detecting occupation using different sensors as data source including various algorithms for estimating occupation rates from this data. Gaining meaningful insights from the data still faces challenges because the potential benefits are not well understood. This study presents a proof-of-concept of an indoor occupation information system, following the design science methodology. We review various types of sensor data that are typically available or easy-to-install in buildings such as offices, classrooms and meeting rooms. This study contributes to current research by incorporating business requirements taken from expert interviews and tackling one of the main barriers for business by designing an affordable system on a common existing infrastructure. We believe that occupation information systems call for further research, in particular also in the context of social distancing because of covid19.
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Optimization of aviation maintenance, repair, and overhaul (MRO) operations has been of high interest in recent years for both the knowledge institutions and the industrial community as a total of approximately $70 billion has been spent on MRO activities in 2018 which represents around 10% of an airline’s annual operational cost (IATA, 2019). Moreover, the aircraft MRO tasks vary from routine inspections to heavy overhauls and are typically characterized by unpredictable process times and material requirements. Especially nowadays due to the unprecedent COVID-19 crisis, the aviation sector is facing significant challenges, and the MRO companies strive to strengthen their competitive position and respond to the increasing demand for more efficient, cost-effective, and sustainable processes. Currently, most maintenance strategies employ preventive maintenance as an industrial standard, which is based on fixed and predetermined schedules. Preventive maintenance is a long-time preferred strategy, due to increased flight safety and relatively simple implementation (Phillips et al., 2010). However, its main drawback stems from the fact that the actual time of failure and the replacement interval of a component are hard to predict resulting in an inevitable suboptimal utilization of material and labor. This has two repercussions: first, the reduced availability of assets, the reduced capacity of maintenance facilities, and the increased costs for both the MRO provider and the operator. Second, the increased waste from an environmental standpoint, as the suboptimal use of assets, is also associated with wasted remaining lifetime for aircraft parts which are replaced, while this isn’t yet necessary (e.g., Nguyen et al., 2019).The recently introduced, condition-based maintenance (CBM) and predictive maintenance (PdM) data-driven strategies aim to reduce maintenance costs, maxi-mize availability, and contribute to sustainable operations by offering tailored pro-grams that can potentially result in optimally planned, just-in-time maintenance meaning reduction in material waste and unneeded inspections.
We present the Stargazing Live! program comprising a planetarium experience and supporting lesson activities for pre-university physics education. The mobile planetarium aims to inspire and motivate learners using real telescope data during the experience. Learners then consolidate their learning by creating conceptual models in the DynaLearn software. During development of the program, content experts and stakeholders were consulted. Three conceptual model lesson activities have been created: star properties, star states and the fusion-gravity balance. The present paper evaluates the planetarium experience plus the star properties lesson activity in nine grade 11 and 12 classes across three secondary schools in the Netherlands. Learners are very positive about the planetarium experience, but they are less able to link the topics in the planetarium to the curriculum. The conceptual modelling activity improves the learners understanding of the causal relationship between the various stellar properties. Future work includes classroom testing of the star states and fusion-gravity balance lessons.
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
Met het groeien van de gemiddelde levensverwachting is ook de uitdaging gegroeid om een ieder zo lang mogelijk een actieve deelnemer van de samenleving te laten zijn. Duurzame zelfstandige mobiliteit is van groot belang voor het functioneren in de samenleving (op werkplek en in thuisomgeving), draagt bij aan het sociaal functioneren en de algemene sociale cohesie. Goede controle over de (dynamische) balans speelt hierbij een grote rol, zijnde de balanshandhaving tijdens het voortbewegen, ook bij gezonde, jonge mensen een continue compromis tussen effectiviteit en veiligheid. Voor ouderen geldt dit nog sterker, daar de gevolgen van een val vele malen ernstiger zijn en ook een grote invloed hebben op de levensverwachting. Mechanismen van handhaving van de dynamische balans in praktische omstandigheden zijn nog grotendeels onbegrepen. Laboratoria staat vaak ver af van praktische condities van de alledaage praktijk. Moderne sensortechnologie opent momenteel een deur naar systematisch onderzoek naar valrisico’s in het dagelijkse leven, echter deze schiet nog te kort in haalbare accuratesse en stabiltiteit over langere metingen. In verschillende projecten wordt momenteel een nieuwe generatie van methoden onderzocht, met als centraal kenmerk hiervan dat bewegingsensoren niet meer als losse onderdelen functioneren, maar in samenhang worden gebruikt. Het kersverse INSTANT project, bijvoorbeeld, onderzoekt hoe huidige bewegingsensoren kunnen worden uitgebreid met een extra sensormodaliteit en ‘meta-datafusion’ algorithmen. Hierdoor kunnen de sensoren elkaars positie waarnemen en naar verwachting een orde meer accuraat meten op een manier die bovendien stabieler is over langere metingen. Aan iets vergelijkbaars wordt gewerkt door collega’s in Torino en Sassari, Italie, zij het met een andere type sensortechnologie. Dit KIEM project onderzoekt in hoeverre beide methoden (en beide onderzoeksclusters) elkaar kunnen versterken door intensief samen te werken. Het plaatsen van een Italiaanse onderzoeker in het INSTANT onderzoekscluster in Enschede gedurende grote delen van een jaar borgt deze samenwerking.
Rotating machinery, such as centrifugal pumps, turbines, bearings, and other critical systems, is the backbone of various industrial processes. Their failures can lead to significant maintenance costs and downtime. To ensure their continuous operation, we propose a fault diagnosis and monitoring framework that leverages the innovative use of acoustic sensors for early fault detection, especially in components less accessible for traditional vibration-based monitoring strategies. The main objective of the proposed project is to develop a fault diagnosis and monitoring framework for rotating machinery, including the fusion of acoustic sensors and physics-based models. By combining real-time monitoring data from acoustic sensors with an understanding of first principles, the framework will enable maintenance practitioners to identify and categorize different failure modes such as wear, fatigue, cavitation, reduced flow, bearing damage, impeller damage, misalignment, etc. In the initial phase, the focus will be on centrifugal pumps using the existing test set-up at the University of Twente. Sorama specializes in acoustic sensors to locate noise sources and will provide acoustic cameras to capture sound patterns related to pump deterioration during various operating conditions. These acoustic signals will then be correlated with the different failure modes and mechanisms that will be described by physics-based models, such as wear, fatigue, cavitation, corrosion, etc. Furthermore, a recently published data set by the Dynamics Based Maintenance research group that includes vibration analysis data and motor current analysis data of various fault scenarios, such as mentioned above, will be used as validation. The anticipated outcome of this project is a versatile framework for a physics-informed acoustic monitoring system. This system is designed to enhance early fault detection significantly, reducing maintenance costs and downtime across a broad spectrum of industrial applications, from centrifugal pumps to turbines, bearings, and beyond.
