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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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Online sensor fault detection based on an improved strong tracking filter.

Lijuan Wang1,2,3, Lifeng Wu4,5,6, Yong Guan7,8,9

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This study introduces an online sensor fault detection method using the evolving Strong Tracking Filter (STCKF). The approach accurately identifies faulty sensors by analyzing estimation residuals against a set threshold.

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Fault Detection and Diagnosis

Background:

  • Online sensor fault detection is crucial for system reliability and safety.
  • Traditional methods may struggle with nonlinear systems and require complex models.
  • Accurate state estimation is fundamental for effective fault detection.

Purpose of the Study:

  • To develop and validate a novel online sensor fault detection method.
  • To leverage the evolving Strong Tracking Filter (STCKF) for enhanced accuracy in nonlinear systems.
  • To establish a robust fault detection mechanism using residuals and a predefined threshold.

Main Methods:

  • Utilizing the evolving Strong Tracking Filter (STCKF) for state estimation.
  • Employing the cubature rule for improved state estimation accuracy in nonlinear scenarios.
  • Analyzing residuals (difference between estimated and true values) as fault indicators.
  • Implementing a threshold comparison for binary fault decision (faulty/not faulty).

Main Results:

  • The proposed STCKF-based method effectively detects sensor faults online.
  • The cubature rule enhances the accuracy of state estimation, particularly in nonlinear dynamics.
  • Simulations on a drum-boiler model demonstrate the algorithm's practical effectiveness.
  • The method requires only a nominal plant model for operation.

Conclusions:

  • The proposed online sensor fault detection method based on STCKF is effective and accurate.
  • The integration of the cubature rule improves state estimation for nonlinear systems.
  • This approach offers a reliable solution for sensor fault diagnosis with minimal model dependency.