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Set-membership methodology for model-based prognosis.

Basma Yousfi1, Tarek Raïssi2, Messaoud Amairi1

  • 1MACS Laboratory : Modeling, Analysis and Control of Systems LR16ES22, National Engineering School of Gabes (ENIG), University of Gabes, Tunisia..

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Summary
This summary is machine-generated.

This study introduces a novel prognosis method for predicting the Remaining Useful Life (RUL) in dynamical systems. It utilizes singular perturbation techniques and interval observers for accurate degradation assessment and RUL estimation.

Keywords:
DamageInterval observersPrognosisRemaining useful lifeSingularly perturbed systems

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Area of Science:

  • Dynamical Systems Analysis
  • Prognostics and Health Management (PHM)
  • Control Theory

Background:

  • Accurate prediction of Remaining Useful Life (RUL) is crucial for effective maintenance and operational planning in dynamical systems.
  • Existing methods may not adequately capture the slow-evolving degradation behaviors inherent in many systems.
  • Model-based prognosis offers a robust framework for RUL estimation.

Purpose of the Study:

  • To develop a model-based prognosis methodology for predicting the RUL of dynamical systems.
  • To incorporate singular perturbation techniques to effectively model slow degradation dynamics.
  • To design an interval observer for robust state estimation under bounded noise and disturbances.

Main Methods:

  • Decoupling the full-order system into slow and fast subsystems using singular perturbation theory.
  • Designing interval observers for both subsystems to handle bounded measurement noise and disturbances.
  • Modeling system degradation as a polynomial and estimating its parameters using ellipsoid algorithms.
  • Predicting RUL through interval evaluation of the degradation model over a specified time horizon.

Main Results:

  • The proposed methodology successfully predicts the RUL of dynamical systems by accounting for slow degradation.
  • Interval observers provide robust state estimation, crucial for accurate prognosis.
  • Polynomial degradation modeling with ellipsoid parameter estimation demonstrates effectiveness.
  • A numerical example validates the practical applicability of the technique.

Conclusions:

  • The presented model-based prognosis approach effectively estimates RUL in dynamical systems.
  • Singular perturbation and interval observers are key components for handling system complexity and uncertainties.
  • This technique offers a valuable tool for predictive maintenance and system health management.