A scalable and rapidly deployable fault detection framework for building heating systems is presented. Unlike existing data-intensive machine learning approaches, a SARIMAX-based concept was implemented to address challenges with limited data availability after commissioning of the plant. The effectiveness of this framework is demonstrated on real-world data from multiple solar thermal systems, indicating potential for extensive field tests and applications for broader systems, including heat pumps and district heating.