  In this paper PCA and D-PCA techniques are applied for the design of a Data
Driven diagnostic Fault Isolation (FI) and Fault Estimation (FE) scheme for 18
primary sensors of a semi-autonomous aircraft. Specifically,
Contributions-based, and Reconstruction-based Contributions approaches have
been considered. To improve FI performance an inference mechanism derived from
evidence-based decision making theory has been proposed. A detailed FI and FE
study is presented for the True Airspeed sensor based on experimental data.
Evidence Based Filtering (EBF) showed to be very effective particularly in
reducing false alarms.
