Log-based predictive maintenance in discrete parts manufacturing

Clemens Gutschi, Nikolaus Furian, Dietmar Neubacher

Publikation: KonferenzbeitragAbstractForschungBegutachtung

Abstract

The performance of discrete parts manufacturing systems is heavily influenced by unplanned machine breakdowns. Predictive maintenance allows for the conversation of unplanned machine breakdowns to scheduled corrective maintenance actions. We present a data-driven log-based approach for estimating the probability of machine breakdowns during specified time intervals in the future. Machine learning algorithms are utilized for a specific use-case which is based on real-world data-sets including machine log messages, event logs and operational information. We will present an overview on predictive maintenance strategies as well as applied data-preparation, feature-engineering and machine learning methods for estimating the remaining useful lifetime. The comparison of two different methods for RUL-estimation is indicating that machine failures can be predicted up to 168 hours in advance with promising precision and hit-rate.
Titel in ÜbersetzungLog-based predictive maintenance in discrete parts manufacturing
Originalspracheenglisch
PublikationsstatusVeröffentlicht - Jun 2019
Veranstaltung30th European Conference on Operational Research - University College Dublin, Dublin, Irland
Dauer: 23 Jun 201926 Jun 2019
https://www.euro2019dublin.com/

Konferenz

Konferenz30th European Conference on Operational Research
KurztitelEURO 2019
LandIrland
OrtDublin
Zeitraum23/06/1926/06/19
Internetadresse

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Learning systems
Learning algorithms
Machine breakdown
Manufacturing
Machine learning
Learning methods
Corrective maintenance
Learning algorithm
Maintenance strategy
Preparation
Manufacturing systems

Schlagwörter

    ASJC Scopus subject areas

    • Artificial intelligence
    • !!Industrial and Manufacturing Engineering
    • !!Management Science and Operations Research
    • !!Control and Systems Engineering

    Fields of Expertise

    • Mobility & Production

    Dies zitieren

    Gutschi, C., Furian, N., & Neubacher, D. (2019). Log-based predictive maintenance in discrete parts manufacturing. Abstract von 30th European Conference on Operational Research, Dublin, Irland.

    Log-based predictive maintenance in discrete parts manufacturing. / Gutschi, Clemens; Furian, Nikolaus; Neubacher, Dietmar.

    2019. Abstract von 30th European Conference on Operational Research, Dublin, Irland.

    Publikation: KonferenzbeitragAbstractForschungBegutachtung

    Gutschi, C, Furian, N & Neubacher, D 2019, 'Log-based predictive maintenance in discrete parts manufacturing', Dublin, Irland, 23/06/19 - 26/06/19, .
    Gutschi C, Furian N, Neubacher D. Log-based predictive maintenance in discrete parts manufacturing. 2019. Abstract von 30th European Conference on Operational Research, Dublin, Irland.
    Gutschi, Clemens ; Furian, Nikolaus ; Neubacher, Dietmar. / Log-based predictive maintenance in discrete parts manufacturing. Abstract von 30th European Conference on Operational Research, Dublin, Irland.
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    N2 - The performance of discrete parts manufacturing systems is heavily influenced by unplanned machine breakdowns. Predictive maintenance allows for the conversation of unplanned machine breakdowns to scheduled corrective maintenance actions. We present a data-driven log-based approach for estimating the probability of machine breakdowns during specified time intervals in the future. Machine learning algorithms are utilized for a specific use-case which is based on real-world data-sets including machine log messages, event logs and operational information. We will present an overview on predictive maintenance strategies as well as applied data-preparation, feature-engineering and machine learning methods for estimating the remaining useful lifetime. The comparison of two different methods for RUL-estimation is indicating that machine failures can be predicted up to 168 hours in advance with promising precision and hit-rate.

    AB - The performance of discrete parts manufacturing systems is heavily influenced by unplanned machine breakdowns. Predictive maintenance allows for the conversation of unplanned machine breakdowns to scheduled corrective maintenance actions. We present a data-driven log-based approach for estimating the probability of machine breakdowns during specified time intervals in the future. Machine learning algorithms are utilized for a specific use-case which is based on real-world data-sets including machine log messages, event logs and operational information. We will present an overview on predictive maintenance strategies as well as applied data-preparation, feature-engineering and machine learning methods for estimating the remaining useful lifetime. The comparison of two different methods for RUL-estimation is indicating that machine failures can be predicted up to 168 hours in advance with promising precision and hit-rate.

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    KW - random forest

    KW - ensemble prediction

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    KW - maintenance

    KW - Maintenance Management

    KW - data mining

    KW - feature engineering

    KW - feature selection

    KW - Remaining useful life

    KW - Ensemble prediction

    KW - Random forrest

    KW - condition monitoring

    M3 - Abstract

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