Prognostic analytic models have become a viable way to reduce operational interruptions when sufficient timely data is available and the resultant model is a good predictor. This paper describes a set of evaluation metrics which can characterize model performance as a degradation estimate and as a decision enabler. The model accuracy over time is assessed against a correlation with the remaining useful life. This yields both a prediction accuracy and confidence interval. The decision can be based on the level of confidence around the prediction, which is based on both how far into the future the event is predicted and how well the current health and its deterioration is estimated. With an effective means of evaluating prognostic models, better benchmarks can be established to communicate model effectiveness and appropriately schedule routine service.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Prognostic Model Evaluation Metrics


    Contributors:


    Publication date :

    2023-03-04


    Size :

    1895852 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Metrics and development tools for prognostic algorithms

    Kacprzynski, G.J. / Liberson, A. / Palladino, A. et al. | IEEE | 2004



    Metrics and development tools for prognostic algorithms

    Kacprzynski, G.J. / Liberson, A. / Palladino, A. et al. | Tema Archive | 2004


    11.1206 Metrics and Development Tools for Prognostic Algorithms

    IEEE | British Library Conference Proceedings | 2004