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Related Experiment Video

Updated: Jul 7, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

Automated fault diagnosis in nonlinear multivariable systems using a learning methodology.

A B Trunov1, M M Polycarpou

  • 1Department of Electrical and Computer Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221-0030, USA.

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary

This study introduces a robust fault diagnosis scheme for nonlinear systems, effectively detecting and approximating both state and output faults using adaptive nonlinear filtering. The method ensures reliable fault estimation despite system uncertainties.

Related Experiment Videos

Last Updated: Jul 7, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

Area of Science:

  • Control Engineering
  • System Dynamics
  • Fault Detection and Diagnosis

Background:

  • Nonlinear multi-input-multi-output (MIMO) dynamical systems are susceptible to faults affecting state and output variables.
  • Faults can manifest as abrupt changes or slowly developing (incipient) deviations, requiring sophisticated detection methods.
  • Accurate fault diagnosis is critical for maintaining system performance and safety in complex engineering applications.

Purpose of the Study:

  • To develop a robust fault diagnosis scheme for detecting and approximating state and output faults in nonlinear MIMO systems.
  • To model fault dynamics as nonlinear functions of control inputs and measured outputs.
  • To represent both incipient and abrupt faults with distinct time profiles.

Main Methods:

  • Utilizes on-line approximators for fault estimation.
  • Employs adaptive nonlinear filtering techniques.
  • Models fault functions based on control input and measured output variables.

Main Results:

  • The scheme robustly detects and approximates state and output faults.
  • Demonstrates accurate fault function estimation even with modeling uncertainties.
  • Theoretical results on robustness, fault sensitivity, and stability are rigorously derived.

Conclusions:

  • The proposed robust fault diagnosis scheme is effective for nonlinear MIMO systems.
  • The method provides accurate fault approximation using adaptive nonlinear filtering.
  • Validated through a simulation of a fourth-order satellite model, highlighting practical applicability.