Machine learning and optimization for predictive maintenance based on predicting failure in the next five days

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    3 Scopus citations

    Abstract

    This study proposes a framework to predict machine failures using sensor data and optimize predictive/corrective maintenance schedule. Using historical data, machine learning (ML) models are trained to predict the failure probabilities for the next five days. Multiple algorithms, including feature extraction techniques, selections, and ML models (both regression and classification based) are compared. The machine learning models' output is fed to an optimization model to propose an optimized maintenance policy, and we demonstrate how prediction models can help increase system reliability at lower costs.

    Original languageBritish English
    Title of host publicationICORES 2021 - Proceedings of the 10th International Conference on Operations Research and Enterprise Systems
    EditorsGreg H. Parlier, Federico Liberatore, Marc Demange
    Pages192-199
    Number of pages8
    ISBN (Electronic)9789897584855
    StatePublished - 2021
    Event10th International Conference on Operations Research and Enterprise Systems, ICORES 2021 - Virtual, Online
    Duration: 4 Feb 20216 Feb 2021

    Publication series

    NameICORES 2021 - Proceedings of the 10th International Conference on Operations Research and Enterprise Systems

    Conference

    Conference10th International Conference on Operations Research and Enterprise Systems, ICORES 2021
    CityVirtual, Online
    Period4/02/216/02/21

    Keywords

    • Condition-based maintenance
    • Machine learning
    • Optimization
    • Predictive maintenance

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