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Innovation sequence application to aircraft sensor fault detection: comparison of checking covariance matrix
1Istanbul Technical University Electrical Engineering, Ayazaga, Turkey. caliskan@elk.itu.edu.tr
ISA Transactions
|May 29, 2000
Summary
This study compares Kalman filter innovation sequence covariance matrix algorithms for fault detection in aircraft sensor data. The optimal algorithm demonstrated superior performance in detecting minimum fault rates and reducing detection time.
Area of Science:
- Control Engineering
- Aerospace Engineering
- Signal Processing
Background:
- Kalman filters are crucial for state estimation in dynamic systems.
- Detecting sensor faults is vital for flight control system reliability.
- The innovation sequence covariance matrix provides key information for fault detection.
Purpose of the Study:
- To compare four algorithms for verifying the Kalman filter innovation sequence covariance matrix.
- To evaluate algorithms based on minimum fault rate and detection time.
- To assess the applicability of these algorithms for aircraft sensor fault detection.
Main Methods:
- Implementation of four distinct covariance matrix verification algorithms.
- Application of algorithms to longitudinal aircraft dynamics.
- Fault detection analysis focusing on fault rate and detection latency.
Main Results:
- The optimal algorithm, utilizing a ratio of quadratic forms, showed the best performance.
- Specific algorithms were ranked based on their effectiveness in fault detection.
- The generalized variance algorithm also presented competitive results.
Conclusions:
- The optimal algorithm is recommended for enhanced sensor fault detection in aircraft.
- Algorithm selection should consider the trade-off between fault detection rate and time.
- Findings offer practical guidance for integrating advanced fault detection into flight control systems.