Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.
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Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation. These models are in charge of providing key insights, such as which parameters are important on a specific measured outcome or which parameter values we should expect to observe given a set of input parameters. At the same time, however, these models rely heavily on assumptions (e.g., stationarity) or are “black box” (e.g., deep neural networks), meaning that they lack interpretability of their internal working and can be viewed only in terms of their inputs and outputs. An interpretable alternative to the black box models and with considerably less assumptions is symbolic regression (SR). SR searches for the optimal model structure while simultaneously optimizing the model’s parameters without relying on an a priori model structure. In this work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at high frequencies through the entire flight, in order to uncover meaningful algebraic relationships between the EGT and other measurable engine parameters. The experimental results exhibit promising model accuracy, as well as explainability returning an absolute difference of 3°C compared to the ground truth and demonstrating consistency from an engineering perspective.
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Trustworthy data-driven prognostics in gas turbine engines are crucial for safety, cost-efficiency, and sustainability. Accurate predictions depend on data quality, model accuracy, uncertainty estimation, and practical implementation. This work discusses data quality attributes to build trust using anonymized real-world engine data, focusing on traceability, completeness, and representativeness. A significant challenge is handling missing data, which introduces bias and affects training and predictions. The study compares the accuracy of predictions using Exhaust Gas Temperature (EGT) margin, a key health indicator, by keeping missing values, using KNN-imputation, and employing a Generalized Additive Model (GAM). Preliminary results indicate that while KNN-imputation can be useful for identifying general trends, it may not be as effective for specific predictions compared to GAM, which considers the context of missing data. The choice of method depends on the study’s objective: broad trend forecasting or specific event prediction, each requiring different approaches to manage missing data.
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From the article: "A facile approach for the fabrication of large-scale interdigitated nanogap electrodes (nanogap IDEs) with a controllable gap was demonstrated with conventional micro-fabrication technology to develop chemocapacitors for gas sensing applications. In this work, interdigitated nanogap electrodes (nanogap IDEs) with gaps from 50–250 nm have been designed and processed at full wafer-scale. These nanogap IDEs were then coated with poly(4-vinyl phenol) as a sensitive layer to form gas sensors for acetone detection at low concentrations. These acetone sensors showed excellent sensing performance with a dynamic range from 1000 ppm to 10 ppm of acetone at room temperature and the observed results are compared with conventional interdigitated microelectrodes according to our previous work. Sensitivity and reproducibility of devices are discussed in detail. Our approach of fabrication of nanogap IDEs together with a simple coating method to apply the sensing layer opens up possibilities to create various nanogap devices in a cost-effective manner for gas sensing applications"
MULTIFILE
Ageing-in-place is the preferred way of living for older individuals in an ageing society. It can be facilitated through architectural and technological solutions in the home environment. Dementia poses additional challenges when designing, constructing, or retrofitting housing facilities that support ageing-in-place. Older adults with dementia and their partners ask for living environments that support independence, compensate for declining and vitality, and lower the burden of family care. This study reports the design process of a demonstration home for people with dementia through performing a literature review and focus group sessions. This design incorporates modifications in terms of architecture, interior design, the indoor environment, and technological solutions. Current design guidelines are frequently based on small-scale studies, and, therefore, more systematic field research should be performed to provide further evidence for the efficacy of solutions. The dwellings of people with dementia are used to investigate the many aspects of supportive living environments for older adults with dementia and as educational and training settings for professionals from the fields of nursing, construction, and building services engineering.
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A local operating theater ventilation device to specifically ventilate the wound area has been developed and investigated. The ventilation device is combined with a blanket which lies over the patient during the operation. Two configurations were studied: Configuration 1 where HEPA-filtered air was supplied around and parallel to the wound area and Configuration 2 where HEPA-filtered air was supplied from the top surface of the blanket, perpendicular to the wound area. A similar approach is investigated in parallel for an instrument table. The objective of the study was to verify the effectiveness of the local device. Prototype solutions developed were studied experimentally (laboratory) and numerically (CFD) in a simplified setup, followed by experimental assessment in a full scale mock-up. Isothermal as well as non-isothermal conditions were analyzed. Particle concentrations obtained in proposed solutions were compared to the concentration without local ventilation. The analysis procedure followed current national guidelines for the assessment of operating theater ventilation systems, which focus on small particles (<10 mm). The results show that the local system can provide better air quality conditions near the wound area compared to a theoretical mixing situation (proof-of-principle). It cannot yet replace the standard unidirectional downflow systems as found for ultraclean operating theater conditions. It does, however, show potential for application in temporary and emergency operating theaters
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At this moment, charging your electric vehicle is common good, however smart charging is still a novelty in the developing phase with many unknowns. A smart charging system monitors, manages and restricts the charging process to optimize energy consumption. The need for, and advantages of smart charging electric vehicles are clear cut from the perspective of the government, energy suppliers and sustainability goals. But what about the advantages and disadvantages for the people who drive electric cars? What opportunities are there to support the goals of the user to make smart charging desirable for them? By means of qualitative Co-design methods the underlying motives of early adaptors for joining a smart charging service were uncovered. This was done by first sensitizing the user about their current and past encounters with smart charging to make them more aware of their everyday experiences. This was followed by another generative method, journey mapping and in-depth interviews to uncover the core values that drove them to participate in a smart charging system. Finally, during two co-design sessions, the participants formed groups in which they were challenged to design the future of smart charging guided by their core values. The three main findings are as follows. Firstly, participants are looking for ways to make their sustainable behaviour visible and measurable for themselves. For example, the money they saved by using the smart charging system was often used as a scoreboard, more than it was about theactual money. Secondly, they were more willing to participate in smart charging and discharging (sending energy from their vehicle back to the grid) if it had a direct positive effect on someone close to them. For example, a retiree stated that he was more than willing to share the energy of his car with a neighbouring family in which both young parents work, making them unable to charge their vehicles at times when renewable energy is available in abundance. The third and last finding is interrelated with this, it is about setting the right example. The early adopters want to show people close to them that they are making an effort to do the right thing. This is known as the law of proximity and is well illustrated by a participant that bought a second-hand, first-generation Nissan Leaf with a range of just 80 km in the summer and even less in winter. It isn’t about buying the best or most convenient car but about showing the children that sometimes it takes effort to do the right thing. These results suggest that there are clear opportunities for suppliers of smart EV charging services to make it more desirable for users, with other incentives than the now commonly used method of saving money. The main takeaway is that early adopters have a desire for their sustainable behaviour to be more visible and tangible for themselves and their social environment. The results have been translated into preliminary design proposals in which the law of proximity is applied.
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Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.
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In recent years, a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organizations alike. This article discusses the current state of the art in the adoption of Industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of Industry 4.0 technologies. This article discusses the relevance of the following key Industry 4.0 technologies to construction: data analytics and artificial intelligence, robotics and automation, building information management, sensors and wearables, digital twin, and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This article also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector, a three-phase use of intelligent assets from the point of manufacture up to after build, and a four-staged R&D process for the implementation of smart wearables in a digital enhanced construction site.
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Objectives: To cross-validate the existing peak rate of oxygen consumption (VO2peak) prediction equations in Dutch law enforcement officers and to determine whether these prediction equations can be used to predict VO2peak for groups and in a single individual. A further objective was to report normative absolute and relative VO2peak values of a sample of law enforcement officers in the Netherlands. Material and Methods: The peak rate of oxygen consumption (ml×kg–1×min–1) was measured using a maximal incremental bicycle test in 1530 subjects, including 1068 male and 461 female police officers. Validity of the prediction equations for groups was assessed by comparing predicted VO2peak with measured VO2peak using paired t-tests. For individual differences limits of agreement (LoA) were calculated. Equations were considered valid for individuals when the difference between measured and predicted VO2peak did not exceed ±1 metabolic equivalent (MET) in 95% of individuals. Results: None of the equations met the validity criterion of 95% of individuals having ±1 MET difference or less than the measured value. Limits of agreement (LoAs) were large in all predictions. At the individual level, none of the equations were valid predictors of VO2peak (ml×kg–1×min–1). Normative values for Dutch law enforcement officers were presented. Conclusions: Substantial differences between measured and predicted VO2peak (ml×kg–1×min–1) were found. Most tested equations were invalid predictors of VO2peak at group level and all were invalid at individual levels.
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