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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Prognostic Model Evaluation Metrics


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    04.03.2023


    Format / Umfang :

    1895852 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    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 Archiv | 2004


    11.1206 Metrics and Development Tools for Prognostic Algorithms

    IEEE | British Library Conference Proceedings | 2004