For linear and Gaussian systems, fault detection over a batch of data is well-studied, and analytical solutions exist in a stochastic framework. The parity space approach handles additive faults and can be shown to be equivalent to estimating the state trajectory and then removing its influence on the output sequence. Multiplicative faults in linear systems can be handled using parameter estimation methods, such as the EM-algorithm in combination with the Kalman smoother. For nonlinear and non-Gaussian systems, we propose to estimate the state trajectory and the faults over the data batch using a particle smoother and the EM-algorithm. The result is a generic fault detection and isolation scheme that applies to arbitrary nonlinear and non-Gaussian systems, where the faults are monitored over a sliding window.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fault detection using nonlinear parameter estimation


    Contributors:

    Published in:

    Publication date :

    2011-03-01


    Size :

    289331 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Fault detection and diagnosis in propulsion systems - A fault parameter estimation approach

    Duyar, Ahmet / Eldem, Vasfi / Merrill, Walter et al. | AIAA | 1994


    Neural parameter estimators for hybrid fault diagnosis and estimation in nonlinear systems

    Sobhani-Tehrani, E. / Talebi, H.A. / Khorasani, K. | Tema Archive | 2007


    Recursive Least Squares Parameter Estimation for DC Fault Detection and Localization

    O'Shea, Kellen / Tsao, Bang-Hung / Herrera, Luis et al. | IEEE | 2019


    Improvement of Nonlinear Simulation using Parameter Estimation Techniques

    Kramer, P. / Gimonet, B. | British Library Conference Proceedings | 2000