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Aviation Data Analytics in MRO Operations: Prospects and Pitfalls

As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.

Data Infrastructures Paradigms

In the era of Industry 4.0, data has become a critical driver of innovation and competitiveness in manufacturing. The up-take of technologies such as IoT, AI, and robotics has led to an unprecedented explosion of data, offering transformative opportunities for process optimization, product quality enhancement, predictive maintenance, and customisation. However, realizing these benefits requires effective data collection, storage, and processing, which necessitates robust and adaptable IT infrastructures. This white paper provides a comprehensive overview of four popular data infrastructure paradigms: data lakes, data fabrics, data meshes, and data spaces. Each of these paradigms have their unique focus points and application domains. Metadata serves as a crucial component, enhancing the findability, accessibility, interoperability, and reusability of data. The paper also explores critical building blocks of modern data infrastructures within manufacturing environments, such as OPC UA, brokers, and data catalogues, highlighting their role in enabling effective data sharing, management, and governance.

Off the charts: A comparative analysis of data visualisations in online science news from South Africa and the USA

Terms like ‘big data’, ‘data science’, and ‘data visualisation’ have become buzzwords in recent years and are increasingly intertwined with journalism. Data visualisation may further blur the lines between science communication and graphic design. Our study is situated in these overlaps to compare the design of data visualisations in science news stories across four online news media platforms in South Africa and the United States. Our study contributes to an understanding of how well-considered data visualisations are tools for effective storytelling, and offers practical recommendations for using data visualisation in science communication efforts.

E-mobility

EV Charging Data Management, five issues to solve

The transition towards electric mobility is expected to take off the coming years, as more EV car models access the market and charging infrastructure is being expanded. The expansion of charging infrastructure will have to accelerate to keep pace with the fast-growing need for charging. The coming years will be marked by uncertainty regarding technological developments (batteries, range), charging technologies (e.g. fast charging, inductive), growth of car sharing and autonomous driving and impact on user preferences and charging behaviour Data management is key to the EV market and public parties involved: to be able to adapt quickly to changes and to reduce risks and costs. This paper describes the five most important preconditions for effective data management that allows stakeholders to monitor the performance of their charging infrastructure and to take informed decisions on rollout strategies based on data science research results.

What do we know about the coronavirus, up to this point, and what does this mean for events?

What is known in scientific literature at this point in time about the effects of the measures against the transmission of the coronavirus and what is the meaning of this for the organisers of events?