Hydropower is important in the modern power system due to its ability to quickly adjust production. More frequent use of this ability may lead to increased maintenance needs, highlighting the importance of research in condition monitoring for hydropower. This study suggests a model approach for friction monitoring of the bearings inside the Kaplan turbine’s hub. The approach is developed for when normal and anomalous data exist. The study compares isolation forest (iForest), local outlier factor (LOF), one-class support vector machine (OC-SVM), and Mahalanobis distance (MD) for anomaly detection, where iForest and OC-SVM appear to be good choices due to their robust performance. A moving decision filter (MDF) is fed with the output from the anomaly detection models to classify the data as normal or anomalous. The parameters in the MDF are optimized with Bayesian optimization to increase the performance of the models. The approach is tested using data from two actual hydropower turbines. The study shows that the model approach works for both turbines. However, the parameter optimization must be performed separately for each turbine.