PCA Methods and Evidence Based Filtering for Robust Aircraft Sensor Fault Diagnosis

N. Cartocci; G. Costante; M. R. Napolitano; P. Valigi; F. Crocetti; M. L. Fravolini

doi:10.1109/med48518.2020.9182973

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arXiv · 2212.06688

PCA Methods and Evidence Based Filtering for Robust Aircraft Sensor Fault Diagnosis

N. Cartocci·G. Costante·M. R. Napolitano·P. Valigi·F. Crocetti·M. L. Fravolini

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Abstract

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.

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BibTeX RIS

N. Cartocci, G. Costante, M. R. Napolitano, P. Valigi, F. Crocetti, M. L. Fravolini. 2022-12-13. PCA Methods and Evidence Based Filtering for Robust Aircraft Sensor Fault Diagnosis. https://doi.org/10.1109/med48518.2020.9182973

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