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Related Experiment Video

Updated: Nov 15, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation.

Nicholas Cartocci1, Marcello R Napolitano2, Gabriele Costante1

  • 1Department of Engineering, University of Perugia, Via G. Duranti, 67, 06125 Perugia, Italy.

Sensors (Basel, Switzerland)
|March 3, 2021
PubMed
Summary

Current aviation sensor fault diagnosis methods are insufficient. This study compares data-driven techniques for reliable fault isolation and estimation, enhancing aircraft safety.

Keywords:
Bayesian filteringaircraft safetydata-driven fault diagnosisfault isolation and estimationflight datarobust residual generation

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Area of Science:

  • Aerospace Engineering
  • Control Systems
  • Data-Driven Analytics

Background:

  • Catastrophic aviation events highlight limitations in current sensor fault diagnosis.
  • Need for enhanced reliability and promptness in identifying and estimating sensor failures.

Purpose of the Study:

  • To comparatively analyze data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques.
  • To evaluate the performance of different methods for 14 aircraft sensors, focusing on air-data sensors.

Main Methods:

  • Development of linear regression models to generate primary and transformed residuals.
  • Implementation of directional Mahalanobis distance-based and fault reconstruction-based FI schemes.
  • Proposal of a Bayesian filter bank for real-time fault belief computation.

Main Results:

  • Comparative performance evaluation of FI and FE techniques using flight data with injected artificial faults.
  • Detailed analysis focused on True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors.
  • Validation using data from multiple flights for both training and testing.

Conclusions:

  • Data-driven techniques offer a promising approach to improve sensor fault diagnosis in semi-autonomous aircraft.
  • The comparative analysis provides insights into the effectiveness of different FI/FE methods for critical air-data sensors.
  • Bayesian filters enable in-flight monitoring and belief computation for enhanced fault detection.