This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decision-making. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approximations can be made using the first-order second moment method (FOSM), the first-order reliabilitymethod (FORM), and the inverse first-order reliabilitymethod (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Analytical algorithms to quantify the uncertainty in remaining useful life prediction


    Contributors:


    Publication date :

    2013-03-01


    Size :

    1087804 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Prediction of Mining Railcar Remaining Useful Life

    Rahimdel, Mohammad Javad / Ghodrati, Behzad / Vahed, Amir Taghizadeh | Springer Verlag | 2019


    Prediction of Remaining Useful Life for Aero-Engines

    B, Rounak / J, Manikandan | IEEE | 2021


    Analysis of Data-Driven Prediction Algorithms for Lithium-Ion Batteries Remaining Useful Life

    Jiang, Lin / Xian, Wei Ming / Long, Bing et al. | Tema Archive | 2013