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.
@article{arxiv.2212.06688,
title = {PCA Methods and Evidence Based Filtering for Robust Aircraft Sensor Fault Diagnosis},
author = {N. Cartocci and G. Costante and M. R. Napolitano and P. Valigi and F. Crocetti and M. L. Fravolini},
journal= {arXiv preprint arXiv:2212.06688},
year = {2022}
}