Sensor fault detection in a class of nonlinear systems using modal Kalman filter
Fatemeh Honarmand-Shazilehei1, Naser Pariz1, Mohammad B Naghibi Sistani1
1Department of Electrical Engineering, Ferdowsi University of Mashhad, P.O. Box 91775-1111, Mashhad, Iran.
ISA Transactions
|August 25, 2020
Summary
This study introduces the modal Kalman filter for enhanced sensor fault detection in nonlinear systems. The advanced filter improves accuracy and speed compared to traditional methods by incorporating higher-order terms.
Area of Science:
- Control Systems Engineering
- Signal Processing
- Fault Diagnosis
Background:
- Kalman filters are standard for fault detection in system components.
- Existing methods like the extended Kalman filter have limitations in nonlinear systems.
Purpose of the Study:
- To extend and apply the modal Kalman filter for state estimation and sensor fault detection in nonlinear systems.
- To address limitations of traditional Kalman filters in handling nonlinearities.
Main Methods:
- Utilized an extended modal Kalman filter.
- Incorporated higher-order terms in Taylor expansion, unlike the extended Kalman filter's linear approximation.
- Performed simulations to verify performance.
Main Results:
- The modal Kalman filter demonstrated reduced estimation error compared to the extended Kalman filter.
- Achieved superior accuracy and promptness in sensor fault detection.
- Verified the practicality and effectiveness of the proposed method.
Conclusions:
- The modal Kalman filter is a more effective tool for sensor fault detection in nonlinear systems.
- Its ability to capture higher-order nonlinearities leads to improved performance.
- Simulation results confirm its superiority over conventional Kalman filtering techniques.
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