Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementations
MULTIFILE
Problems of energy security, diversification of energy sources, and improvement of technologies (including alternatives) for obtaining motor fuels have become a priority of science and practice today. Many scientists devote their scientific research to the problems of obtaining effective brands of alternative (reformulated) motor fuels. Our scientific school also deals with the problems of the rational use of traditional and alternative motor fuels.This article focused on advances in motor fuel synthesis using natural, associated, or biogas. Different raw materials are used for GTL technology: biomass, natural and associated petroleum gases. Modern approaches to feed gas purification, development of Gas-to-Liquid-technology based on Fischer–Tropsch synthesis, and liquid hydrocarbon mixture reforming are considered.Biological gas is produced in the process of decomposition of waste (manure, straw, grain, sawdust waste), sludge, and organic household waste by cellulosic anaerobic organisms with the participation of methane fermentation bacteria. When 1 tonne of organic matter decomposes, 250 to 500–600 cubic meters of biogas is produced. Experts of the Bioenergy Association of Ukraine estimate the volume of its production at 7.8 billion cubic meters per year. This is 25% of the total consumption of natural gas in Ukraine. This is a significant raw material potential for obtaining liquid hydrocarbons for components of motor fuels.We believe that the potential for gas-to-liquid synthetic motor fuels is associated with shale and coalfield gases (e.g. mine methane), methane hydrate, and biogas from biomass and household waste gases.
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.
