Machine Learning (ML) has been largely employed to sensor data for predicting the Remaining Useful Life (RUL) of aircraft components with promising results. A review of the literature, however, has revealed a lack of consensus regarding evaluation metrics adopted, the state-of-the-art methods employed for performance comparison, the approaches to address data overfitting, and statistical tests to assess results’ significance. These weaknesses in methodological approaches to experimental design, results evaluation, comparison and reporting of findings can result in misleading outcomes and ultimately produce less effective predictors. Arbitrary choices of approaches for novel method’s evaluation, the potential bias that can be introduced, and the lack of systematic replication and comparison of outcomes might affect the findings reported and misguide future research. For further advances in this area, there is therefore an urgent need for appropriate benchmarking methodologies to assist evaluating novel methods and to produce fair performance rankings. In this paper we introduce an open-source, extensible benchmarking library to address this gap in aerospace prognosis. The library will assist researchers to conduct a proper and fair evaluation of their novel ML RUL predictive models. In addition, it will assist stimulating better practices and a more rigorous experimental design approach across the field. Our library contains 13 state-of-the-art ML methods, 12 metrics for algorithm performance evaluation and tests for statistical significance. To demonstrate the library’s functionalities, we apply it to gas turbine engine prognostic datasets.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An Intelligent Toolkit for Benchmarking Data-Driven Aerospace Prognostics


    Beteiligte:


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    357851 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    A Survey of Data-Driven Prognostics

    Schwabacher, Mark | AIAA | 2005


    Bayesian framework for aerospace gas turbine engine prognostics

    Zaidan, Martha A / Mills, Andrew R / Harrison, Robert F | IEEE | 2013


    Electronic Prognostics Innovations for Applications to Aerospace Systems

    Gerdes, Matthew / Gross, Kenny / Wang, Guang Chao | IEEE | 2023


    Options for Prognostics Methods: A Review of Data-driven and Physics-based Prognostics

    An, D. / Choi, J.H. / Kim, N.H. et al. | British Library Conference Proceedings | 2013