Anomaly detection is the characterization of a normal behavior of a system or process and identification of any deviation from such normal behavior. Anomaly detection of critical systems provides an important financial and client competitive advantage since it gives the decision-maker lead-time and flexibility to manage the health of the system. Fuel systems are complex and mission critical systems that require high operational availability because of the high costs associated with the services they provide. In complex systems, it is not uncommon to monitor a quality-related response which relies on the functional form between several variables using a non-linear relationship. We present in this paper a new monitoring framework for smart fuel systems utilizing outlying observations detection and monitoring using ccharts. The traditional control charts based on the Hotelling's T2 statistic were deficient in detecting SFS anomalies and a new approach was necessary to isolate faulty profiles. The proposed methodology requires a simple quality performance test that can be performed once assembly is completed to assure readiness for client use or completion of a job. The results were tested and validated using scaled data that mimic an actual system. The methodology presented in this paper is scalable and can be applied to a wide range of systems to assess their health from an inspection check to anticipate and avoid failures.