Aircraft Go-Arounds Associated to Vessel Traffic: Hamburg Finkenwerder Case Study

An aircraft go-around is a costly yet safety critical procedure. While there are many reasons to decide that a go-around is necessary, at Hamburg Finkenwerder airport (EDHI) there is a rather peculiar one: vessel traffic crossing the approach path. As both vessels and aircraft transmit their position at regular intervals through the Automatic Identification System (AIS) and Automatic Dependent Surveillance Broadcast (ADS-B) protocols it is possible to identify vessels that can cause problems to the aircraft’s approach. In this work we identified a 10 time higher than average go-around incidence at Finkenwerder airport and were able to find evidence of its relation to large passing vessels. As vessel traffic has a mostly stable course and speed, we found it is possible to predict the passing vessels well ahead of time in order to determine the best approach and reduce the number of go-arounds, allowing to save both fuel and emissions

Trajectory Based Flight Phase Identification with Machine Learning for Digital Twins

Analysis of aircraft trajectory data is used in different applications of aviation research. Areas such as Maintenance, Repair and Overhaul (MRO) and Air Traffic Management (ATM) benefit from a more detailed understanding of the trajectory, thus requiring the trajectory to be divided into the different flight phases. Flight phases are mostly computed from the aircraft’s internal sensor parameters, which are very sensitive and have scarce availability to the public. This is why identification on publicly available data such as Automatic Dependent Surveillance Broadcast (ADS-B) trajectory data is essential. Some of the flight phases required for these applications are not covered by state-of-the-art flight phase identification on ADS-B trajectory data. This paper presents a novel machine learning approach for more detailed flight phase identification. We generate a training dataset with supervised simulation data obtained with the X-plane simulator. The model combines K-means clustering with a Long Short-Term Memory (LSTM) network, the former allows the segmentation to capture transitions between phases more closely, and the latter learns the dynamics of a flight. We are able to identify a larger variety of phases compared to state of the art and adhere to the International Civil Aviation Organisation (ICAO) standard

Proximity to Sports Facilities and Sports Participation for Adolescents in Germany

Objectives - To assess the relationship between proximity to specific sports facilities and participation in the corresponding sports activities for adolescents in Germany. Methods - A sample of 1,768 adolescents aged 11–17 years old and living in 161 German communities was examined. Distances to the nearest sports facilities were calculated as an indicator of proximity to sports facilities using Geographic Information Systems (GIS). Participation in specific leisure-time sports activities in sports clubs was assessed using a self-report questionnaire and individual-level socio-demographic variables were derived from a parent questionnaire. Community-level socio-demographics as covariates were selected from the INKAR database, in particular from indicators and maps on land development. Logistic regression analyses were conducted to examine associations between proximity to the nearest sports facilities and participation in the corresponding sports activities. Results - The logisitic regression analyses showed that girls residing longer distances from the nearest gym were less likely to engage in indoor sports activities; a significant interaction between distances to gyms and level of urbanization was identified. Decomposition of the interaction term showed that for adolescent girls living in rural areas participation in indoor sports activities was positively associated with gym proximity. Proximity to tennis courts and indoor pools was not associated with participation in tennis or water sports, respectively. Conclusions - Improved proximity to gyms is likely to be more important for female adolescents living in rural areas

Ontology-based Process Reengineering To Support Digitalization Of MRO Operations: Application To An Aviation Industry Case

After sales services are characterized by interlinked service provider and customer operations. Furthermore, proper management of data related to the physical product and its lifecycle is essential for proposition of value-adding services. In this article, we present a novel ontology-based approach for improvement of maintenance, repair and overhaul (MRO) processes, where the ontology captures information of industry standards and product-related data. By clearly defining relations, the ontology supports the digitalization of operations and utilization of data in operational processes of different stakeholders. The approach was developed and demonstrated by investigating data of a case from aviation MRO industry.ISSN:2212-827

Semantic Technologies for the Data Model of the Digital Twin

During the lifecycle of an aircraft a huge amount of data is produced. Data is generated directly by the systems of the aircraft, e.g. through sensors and computers. Additionally, offline information is produced such as product documentation and records of maintenance, repair and overhaul (MRO) activities. This data can be valuable for different stakeholders to plan and optimize their processes. During repair processes the extent of damage as well as previous repairs of the affected part are important information to the maintenance and repair organization. However, this kind of information is often not known to the organization until a damaged component arrives at the facility. In case of historical information, it is sometimes not available at all. Furthermore, the heterogeneity of data sources and types as well as the necessity of interaction between different data bases pose a problem to building an efficient data network, which would allow access to data to all stakeholders in the MRO service chain. In recent years, the development of virtual representations of physical products and its environment, socalled Digital Twins, have gained increasing attention in the industry. One of the key roles of this technology is the implementation of product data management systems. Semantic web approaches have been demonstrated to be methods for building networks, which enable efficient data management. In this work, key contributors of the component lifecycle data network are defined and modeled in ontologies, which are then transferred into a graph data base. This includes event, damage and process ontologies, as well as a lifecycle ontology which links the information over time. This approach of using semantic technologies establishes database interoperability and, by logically linking the data, allows analyzing relationships as well as inferring new knowledge, e.g. additional sources of repair input data for more efficient planning of maintenance activities. The advantages of an integrated data network such as improved maintenance capabilities are demonstrated, analyzing the processes surrounding the visual assistance system for manual scarf repairs developed in a mutual research project with an industrial partner. The model allows to take advantage of already existing data sources and identifies potential gaps in the data network, in order to improve MRO processes. Also, the data model can serve as a basis to further develop other Digital Twin functionalities such as predictive maintenance applications

Matching Processes and Data for Improvement of Operational Performance in Aviation MRO

The performance of aviation Maintenance, Repair and Overhaul (MRO) tasks is characterized by a high share of manual work. Additionally, airlines and MRO organizations have to keep record of every maintenance task and transaction between companies. In order to stay competitive in the aviation MRO market, organizations are required to improve their operational performance and utilize their resources in a time- and cost-efficient way. To achieve the targeted efficiency, organizations run dedicated improvement projects. These improvement projects range from identifying, documenting and fine-tuning existing processes to a radical business process reengineering. The projects are often part of company-internal digitalization initiatives. When evaluating processes, it is essential to consider the whole organization and the connection between adjacent operations. Otherwise, an individual process might run optimal but up- and downstream tasks are executed in an inefficient way. In this context, digitalization opportunities, which enable vertical and horizontal integration of operations, need to be taken into account. They offer means to link manual tasks with documentation and data to use this information. In order to support aviation MRO organizations in improving their operations and implementing digital solutions, a dedicated methodology is developed and presented in the paper. Central element of the methodology is a defined architecture of MRO service related tasks and process data combinations, which is based on an ontological data model. The task-data combinations are developed based on information collected in a mutual research project with an industry partner. The application of a visual assistance system for manual scarf repairs of aircraft composite components serves as case study for this project. The application of the methodology enables an MRO organization – after mapping their processes and grouping tasks according to the introduced scheme – to match their operational tasks with the presented ontological data structure. The subsequent re-design of operational processes and data flows facilitates the utilization of collected information, the integration of operations and the achievement of an integrated optimum of the operational performance. The research approach and the application of the methodology are illustrated in FIGURE 1. The presented methodology contributes to research in process improvement, process reengineering and data-driven improvement of aviation MRO. The contribution lies in the integrated consideration of processes and data, the holistic approach – compared to optimization of particular aspects of MRO operations – and taking into account the characteristics of aviation MRO industry

DEVELOPMENT OF A DIGITAL TWIN FOR AVIATION RESEARCH

The application of digital twins is increasing in several fields. Mirroring the current state of the asset and making predictions of the future state are the main purposes of digital twins. Inside the German Aerospace Center (DLR), an internal project is set up to find methods, technologies and processes for digital twins. Several institutes are contributing to the project, including institutes in the IT domain like the Institute of Software Methods for Product Virtualization or the Institute for Software Technology on one side, and the aviation engineering domain on the other side, e.g. the Institute of Flight Systems, the Institute of Composite Structures and Adaptive Systems and the Institute of Maintenance, Repair and Overhaul. In order to demonstrate the capabilities and identify new development opportunities of digital twins, three different use cases are defined. These use cases include the virtual product house, the virtual engine and the research aircraft. For the research aircraft use case, the digital twin can be seen as a research tool within the organization. The research questions of the project are addressing several information technology related issues like data formats, data sizes, data storage concepts, provenance, and security. Additionally, the project addresses the definitions of the digital twin, the digital thread, and the application layer as well as a common digital twin vision. The next steps in the project are the implementation and demonstration of first prototypes for the individual use cases. This paper gives an overview over the project results and the developments for digital twins. The aim is to digitally map aircraft and their components with all their characteristics and relevant data

Trajectory Based Flight Phase Identification with Machine Learning for Digital Twins

Analysis of aircraft trajectory data is used in different applications of aviation research. Areas such as Maintenance, Repair and Overhaul (MRO) and Air Traffic Management (ATM) benefit from a more detailed understanding of the trajectory, thus requiring the trajectory to be divided into the different flight phases. Flight phases are mostly computed from the aircraft’s internal sensor parameters, which are very sensitive and have scarce availability to the public. This is why identification on publicly available data such as Automatic Dependent Surveillance Broadcast (ADS-B) trajectory data is essential. Some of the flight phases required for these applications are not covered by state-of-the-art flight phase identification on ADS-B trajectory data. This paper presents a novel machine learning approach for more detailed flight phase identification. We generate a training dataset with supervised simulation data obtained with the X-plane simulator. The model combines K-means clustering with a Long Short-Term Memory (LSTM) network, the former allows the segmentation to capture transitions between phases more closely, and the latter learns the dynamics of a flight. We are able to identify a larger variety of phases compared to state of the art and adhere to the International Civil Aviation Organisation (ICAO) standard

Digital Twins Storage and Application Service Hub (Twinstash)

The digital twins storage and application service hub, in short twinstash, is a software system aiming to provide a basis for Digital Twins of DLR research aircraft. Initialized in the context of the DigTwin project and its successor project DigECAT, its current stable prototype supports the upload, download, and search for flight sensor data and metadata both programmatically via python and graphically via a browser-based user interface. The latter additionally allows to quickly and intuitively navigate through the data. It provides rich possibilities to visualize and filter flight trajectories as well as sensor data. In this paper, we give an in-depth view on the twinstash software system. We provide details on its architecture, continuous integration and deployment, authentication mechanisms, data model, search, main features of its graphical user interface, and its python client