FDIA System for Sensors of the Aero-Engine Control System Based on the Immune Fusion Kalman Filter
| Autor: | Xiaobao Han, Ruiqian Sun, Linfeng Gou |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
020301 aerospace & aeronautics
0209 industrial biotechnology Article Subject Artificial immune system Computer science Estimation theory General Mathematics General Engineering 02 engineering and technology Kalman filter Engineering (General). Civil engineering (General) Fault (power engineering) Noise 020901 industrial engineering & automation 0203 mechanical engineering Control theory Control system QA1-939 Transient (oscillation) TA1-2040 Particle filter Mathematics |
| Zdroj: | Mathematical Problems in Engineering, Vol 2021 (2021) |
| ISSN: | 1563-5147 1024-123X |
| Popis: | The Kalman filter plays an important role in the field of aero-engine control system fault diagnosis. However, the design of the Kalman filter bank is complex, the structure is fixed, and the parameter estimation accuracy in the non-Gaussian environment is low. In this study, a new filtering method, immune fusion Kalman filter, was proposed based on the artificial immune system (AIS) theory and the Kalman filter algorithm. The proposed method was used to establish the fault diagnosis, isolation, and accommodation (FDIA) system for sensors of the aero-engine control system. Through a filtering calculation, the FDIA system reconstructs the measured parameters of the faulty sensor to ensure the reliable operation of the aero engine. The AIS antibody library based on single sensor fault was constructed, and with feature combination and library update, the FDIA system can reconstruct the measured values of multiple sensors. The evaluation of the FDIA system performance is based on the Monte Carlo method. Both steady and transient simulation experiments show that, under the non-Gaussian environment, the diagnosis and isolation accuracy of the immune fusion Kalman filter is above 95%, much higher than that of the Kalman filter bank, and compared with the Kalman particle filter, the reconstruction value is smoother, more accurate, and less affected by noise. |
| Databáze: | OpenAIRE |
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