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.