This paper studies a multi-model-based hybrid Kalman filter fault diagnosis method. This method uses a non-linear airborne model to adapt to the level of engine degradation, and uses probability density functions to perform hypothesis testing. It can effectively solve the problem of threshold selection and evaluation. The paper establishes the working state model of aero engine. By analyzing various fault situations of sensors, components and actuators, fault-free filters and filter banks of sensors, components and actuators are constructed. By using a Gaussian density function recursive algorithm to locate the fault, the credibility ranking of the fault occurrence site is generated to achieve the purpose of fault isolation. Simulation results show that the algorithm can achieve effective detection and isolation of aero-engine gas path faults, and meet the real-time requirements of airborne computers.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multiple Model-Based Hybrid Kalman Filter for Fault Diagnosis of Jet Engines


    Contributors:


    Publication date :

    2020-07-01


    Size :

    2942374 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Sensor fault diagnosis for flight control system based on Cubature Kalman filter

    Wenkai, Fei / Jie, Xia / Guang, Ouyang et al. | IEEE | 2014



    Switch Machine Fault Diagnosis Method Based on Kalman Filter and Support Vector Machines

    Li, Xiang / Qin, Yong / Wang, Zhipeng et al. | TIBKAT | 2020


    Switch Machine Fault Diagnosis Method Based on Kalman Filter and Support Vector Machines

    Li, Xiang / Qin, Yong / Wang, Zhipeng et al. | Springer Verlag | 2020