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Related Experiment Videos

Sensor fault diagnosis for nonlinear processes with parametric uncertainties.

Srinivasan Rajaraman1, Juergen Hahn, M Sam Mannan

  • 1Mary Kay O'Connor Process Safety Center, Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3122, USA.

Journal of Hazardous Materials
|November 22, 2005
PubMed
Summary

This study presents a novel fault detection and reconstruction method for nonlinear systems, even with unknown process parameters. The technique accurately identifies and reconstructs sensor faults without extensive historical data.

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Area of Science:

  • Control Systems Engineering
  • Nonlinear System Analysis
  • Fault Detection and Diagnosis

Background:

  • Sensor faults in nonlinear systems pose significant challenges, especially with parameter uncertainties.
  • Accurate fault detection and reconstruction are crucial for system reliability and safety.
  • Existing methods often require extensive historical data or prior fault information.

Purpose of the Study:

  • To develop a fault detection, discrimination, and reconstruction technique for nonlinear systems.
  • To address the challenge of interacting faults and parameter uncertainties.
  • To create a method that minimizes reliance on historical operational data.

Main Methods:

  • A fault diagnosis algorithm comprising nonlinear estimators for parameter estimation and a fault isolation/identification filter.

Related Experiment Videos

  • Decoupling parameter estimation and fault reconstruction by performing them at different time scales.
  • Utilizing a more frequent rate for fault diagnosis compared to parameter estimation.

    • Main Results:

    • Successful detection, discrimination, and reconstruction of sensor faults in nonlinear systems.
    • Demonstrated accuracy in fault reconstruction under realistic assumptions.
    • Validation of the methodology on a simulated chemical process with nonlinear dynamics.

    Conclusions:

    • The proposed technique effectively handles sensor faults in nonlinear systems with parameter uncertainty.
    • The time-scale separation approach enables accurate fault reconstruction without extensive prior data.
    • The method shows promise for real-world applications in chemical processes and other nonlinear systems.