Littering in urban areas poses a serious threat across the globe, with direct consequences on human health and climate change. Litter pieces such as soda cans or plastic bags left in the environment destroy nature by, for example, polluting water or contaminating soil. Moreover, animals consume these non-digestible pieces. Once they are washed away into the ocean, both land animals and fish ingest plastic pieces. Not only does this destroy the biodiversity, but toxic microplastics make their entrance in the human food chain as well. In light of these problems, there is a need for a rapid and flexible litter cleanup system.

The main bearing is a critical wind turbine drivetrain component, and its failure can cause the turbine shutdown and expensive repair. As the main bearing degrades, its temperature increases, indicating health deterioration of the component. Various methods proposed in the literature employ physics-based normal behavior models (NBMs) using Standard Supervisory Control And Data Acquisition (SCADA) data as input to energy conservation-based equations to model the main bearing temperature. These methods analyze the difference between the measured and the modeled temperature, referred to as residual. These residuals are used as health indicators (HIs) for assessing the condition of the component. Physics-based NBMs utilizing SCADA data have been successfully used for fault detection of the main bearing. However, the application of these methods for degradation trend monitoring have not yet received attention. The primary reason for the premature failure of wind turbine components is attributed to the variability of the wind conditions. However, current NBM methods are based solely on the mean value records and do not consider the variations within the 10-minute time frame. Furthermore, seasonal fluctuations in operating conditions can adversely affect the obtained degradation trend. The main objective of this thesis is to improve physics-based NBM employing SCADA data to monitor the degradation trend of the main bearing. The proposed approach uses a physics-based NBM available in the literature as the baseline. It aims to increase the monotonicity and reduce the dispersion of the developed HI to enable accurate degradation trend monitoring. To achieve this objective, the proposed method takes into account seasonal variations and variability of operating conditions within the 10-minute SCADA time frame when modeling the main bearing temperature. To mitigate the impact of seasonal changes on the HI, the proposed method develops multiple physics-based NBMs corresponding to monthly time windows. To take into account the variability of the operating conditions, the main bearing temperature is modeled by performing a Monte Carlo simulation using the SCADA data mean and standard deviation values. In this case, the HI is defined by the data density within a threshold region. Two case studies are conducted to demonstrate the advantages of the proposed method compared to the baseline approach. The results show that with the proposed approach, the seasonality effects are reduced by more than 50%, as measured through cross-correlation metric with the ambient temperature, the HI monotonicity increases by 260% as measured by the Mann-Kendall τ monotonicity metric, and the dispersion reduces by 30% and 35%, as evaluated by the Mean Square Error and a noise metric obtained using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise approach.

The electricity generation from wind has seen significant growth over the past two decades with a compound annual growth rate of over 21%, and this trend is projected to continue, aligning with the goals of the European Green Deal. In the context of offshore wind turbines, operational and maintenance costs can account for up to 30% of the total costs throughout the project's lifecycle. This highlights the need for the development of efficient condition monitoring methods aimed at mitigating maintenance expenses. The gearbox is one of the most failure-prone components in wind turbines, leading to extended downtimes and substantial financial outlays. When the components of the gearbox degrade, the heat generated within the gearbox increases. This increase leads to higher oil temperatures, making the temperature signal a suitable indicator of the gearbox health condition. The main objective of this thesis is to design a physics-based normal behaviour model (NBM) for the estimation of the wind turbine gearbox oil temperature and to use field data to validate its effectiveness. The energy conservation principle is applied to the gearbox to formulate an equation for the calculation of the gearbox oil temperature. To account for some unidentified design specifications of the gearbox, the equation is fitted to available historical data from the Supervisory Control and Data Acquisition (SCADA) system to determine the unknown parameters. The model uses as input an array of operational measured signals available in the SCADA system, including rotor speed, power output, nacelle temperature and inlet oil temperature, which is the temperature of the oil after running through the cooling system. A case study demonstrates the model's effectiveness in accurately predicting the gearbox oil temperature with a mean absolute percentage error of 0.75%. In order to investigate how well the physical characteristics of the gearbox are represented by the model, the coefficients of the energy balance equation derived from fitting it to the field SCADA data are compared to the parameters calculated using known characteristics of a reference gearbox. The comparison results indicate that the model's parameters are generally within the same order of magnitude as the reference values. To provide a benchmark, the performance of the physics-based model is also compared to that of two data-driven models using artificial neural networks (ANNs) for temperature prediction proposed in the literature. The results show that the proposed physics-based model outperforms both ANN models, achieving 14% and 59% lower root mean squared error (RMSE) and a reduction in time required for training of 70% and 95% when compared to the two ANN NBMs respectively.