Scarce failure data often causes unreliable results when making predictions concerning Remaining Useful Life (RUL). This study explores the use of deep generative models (DGMs) for augmenting turbofan engine datasets by CMAPSS to improve these RUL predictions. By implementing Conditional Tabular GANs (CTGAN) and Tabular Variational Autoencoders (TVAE), synthetic data is generated and validated using statistical metrics such as Wasserstein distance and Kolmogorov-Smirnov tests. Then, these new datasets are used in several compositions of both real and synthetic data to train regressors and subsequently let them make RUL predictions. The regressors, such as Random Forest Regressors (RFR) and Convolutional Neural Networks (CNN), evaluate performance improvements through RMSE and MAE metrics. Results indicate that adding synthetic data improves prediction robustness, particularly when data is limited. This highlights the potential of DGMs for Prognostics and Health Management (PHM) applications.

Assembly, Integration and Verification (AIV) in space makes launching geosynchronous satellites faster and significantly cheaper in the long term. A space-tug is launched into space to perform AIV there. It assembles a standardised satellite consisting of several modules. The modules are designed in such a way that the required subsystems for a communication satellite are incorporated in the modules. Examples of these modules are a propulsion module, a solar array module and a computer module. Due to the standardised modules, testing time and costs can be reduced significantly. This ensures a delivery time of maximum one year, which is the time from order until operations in space. The modules are efficiently packed and connected to external beams in the launch vehicle, to make sure that two satellites can be launched simultaneously. The external beams take up the extreme loads that occur during launch. This decreases the dry mass of the satellite, as the modules do not need as much structural mass. The subsystem design and structural analysis result in a drymass of 1847 kg per satellite. Next to the two satellites, a refuelling tank is added in the launch vehicle to refuel the tug. The tug requires 2921 kg of fuel to transfer the two satellites and go back to its initial state. Due to the modularity of the satellites, the lifetime of the satellites can be increased. Regarding the economic feasibility of the mission, a full return on investment is expected after 15 years of operations in base case scenario.