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Related Experiment Video

Updated: Sep 27, 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-Driven Fault Diagnosis Techniques: Non-Linear Directional Residual vs. Machine-Learning-Based Methods.

Nicholas Cartocci¹, Marcello R Napolitano², Francesco Crocetti¹

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

Sensors (Basel, Switzerland)

|April 12, 2022

Summary

This summary is machine-generated.

This study introduces a novel non-linear additive model for fault diagnosis, improving accuracy by addressing system nonlinearities. The method enhances fault detection and isolation, outperforming traditional and machine learning approaches.

Keywords:

additive model anomaly detection fault estimation fault isolation multivariate adaptive regression splines non-linear residual-based technique time-dependent directional residuals

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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

Area of Science:

Systems Engineering
Control Theory
Data Science

Background:

Traditional fault diagnosis relies on linearity assumptions, leading to errors with system nonlinearities.
Machine learning (ML) offers improvements but requires extensive, representative faulty data and can overfit.

Purpose of the Study:

To develop a fault diagnosis method robust to system nonlinearities.
To improve fault detection and isolation accuracy compared to existing methods.

Main Methods:

A non-linear additive model is proposed to capture signal redundancy.
Multivariate Adaptive Regression Splines (MARS) identifies non-linear relationships from data.
Linearization and a fault signature matrix enable sensor isolation via angular distance measurement.

Main Results:

The proposed method effectively characterizes non-linear relationships.
Demonstrated accurate fault isolation and estimation using real aircraft flight data.
Outperformed state-of-the-art machine learning-based fault diagnosis algorithms in quantitative analysis.

Conclusions:

The non-linear additive model provides a robust and accurate approach to fault diagnosis.
This method mitigates limitations of linear models and data-hungry ML techniques.
Offers a promising alternative for reliable fault detection in complex systems.