The article addresses data-driven fault detection in commercial aircraft gas turbine engines in the framework of multi-sensor information fusion and symbolic dynamic filtering. The hierarchical decision and control structure, adopted in this article, involves construction of composite patterns, namely, atomic patterns extracted from single sensors, and relational patterns representing cross-dependence between a pair of sensors. While the underlying theories are presented along with necessary assumptions, the proposed method is validated on the NASA C-MAPSS simulation test bed of aircraft gas turbine engines; both single-fault and multiple-fault scenarios have been investigated. Since aircraft engines undergo natural degradation during the course of their normal operation, the issue of distinguishing between a fault and natural degradation is also addressed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-sensor information fusion for fault detection in aircraft gas turbine engines


    Contributors:


    Publication date :

    2013-12-01


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Symbolic identification for fault detection in aircraft gas turbine engines

    Chakraborty, S. / Sarkar, S. / Ray, A. | Tema Archive | 2012


    Advanced fault detection and isolation methods for aircraft turbine engines

    Dettoff, R.L. / Hall, W.E. jun. | Tema Archive | 1978


    Symbolic identification for fault detection in aircraft gas turbine engines

    Chakraborty, S / Sarkar, S / Ray, A | SAGE Publications | 2012