In this project, methods for feature selection and data subset selection will be developed that leverage the advantages of hybrid models, enabling more accurate contextual anomaly detection while making it unnecessary to collect and store all data. The selected features and data will be combined with existing machine learning methods to obtain accurate preventive maintenance, ensuring low maintenance costs and high system availability.