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Related Experiment Video

Updated: Jun 23, 2026

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

Fault detection and diagnosis based on modeling and estimation methods.

Sunan Huang¹, Kok Kiong Tan

¹Department of Electrical and Computer Engineering, National University of Singapore, Singapore 120611, Singapore.

IEEE Transactions on Neural Networks

|April 25, 2009

Summary

This study introduces a novel fault detection and diagnosis method for nonlinear systems. It utilizes radial basis function (RBF) neural networks for accurate fault modeling and identification, even with uncertainties.

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Design and Analysis for Fall Detection System Simplification

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Last Updated: Jun 23, 2026

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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Published on: October 28, 2022

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

Control Systems Engineering
Artificial Intelligence
Nonlinear Dynamics

Background:

Nonlinear systems are prone to faults, necessitating robust detection and diagnosis methods.
Modeling uncertainties in nonlinear systems complicate fault analysis.
Existing methods may struggle with the complex dynamics and uncertainties inherent in these systems.

Purpose of the Study:

To develop an effective fault detection and diagnosis scheme for nonlinear systems with modeling uncertainties.
To leverage neural networks for approximating complex system dynamics and fault characteristics.
To enable accurate identification of failure modes.

Main Methods:

Design of a nonlinear observer incorporating Radial Basis Function (RBF) neural networks.
Utilizing a first RBF network to approximate unknown nonlinear system dynamics.
Employing a second, triggered RBF network to capture fault function nonlinearities.
Fault model generation using the second neural network (NN) for failure mode comparison.

Main Results:

The proposed observer effectively monitors for faults in nonlinear systems.
RBF neural networks accurately approximate system dynamics and fault characteristics.
The generated fault model facilitates reliable identification of failure modes.
Simulation results validate the effectiveness of the proposed fault detection and diagnosis scheme.

Conclusions:

The presented RBF neural network-based observer provides a robust solution for fault detection and diagnosis in uncertain nonlinear systems.
The method accurately models and identifies faults, enhancing system reliability.
This approach offers a promising direction for fault-tolerant control in complex dynamic systems.