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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

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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

Summary

This summary is machine-generated.

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 filtering aircraft safety data-driven fault diagnosis fault isolation and estimation flight data robust residual generation

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Last 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

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

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Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

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.