Log-based predictive maintenance in discrete parts manufacturing

Clemens Gutschi; Nikolaus Furian; Josef Suschnigg; Dietmar Neubacher; Siegfried Vössner

doi:10.1016/j.procir.2019.02.098

Log-based predictive maintenance in discrete parts manufacturing

Clemens Gutschi, Nikolaus Furian, Josef Suschnigg, Dietmar Neubacher, Siegfried Vössner

Publikation: Beitrag in einer Fachzeitschrift › Konferenzartikel › Begutachtung

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 approach for estimating the probability of machine breakdown during specified time interval 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. The paper describes applied data-mining, feature-extraction and machine learning methods and concludes with results indicating that machine failures can be reliably predicted up to 168 hours in advance.

Originalsprache	englisch
Seiten (von - bis)	528-533
Seitenumfang	6
Fachzeitschrift	Procedia CIRP
Jahrgang	79
DOIs	https://doi.org/10.1016/j.procir.2019.02.098
Publikationsstatus	Veröffentlicht - 2019
Veranstaltung	Intelligent Computation in Manufacturing Engineering: Innovative and Cognitive Production Technology and Systems - Island of Ischia, Ischia, Italien Dauer: 18 Juli 2018 → 20 Juli 2018 http://www.icme.unina.it/ICME%2018/ICME_14.htm

Schlagwörter

Prädiktion
Restlebensdauer
random forest
ensemble prediction
Maschinelles Lernen

ASJC Scopus subject areas

Artificial intelligence
Wirtschaftsingenieurwesen und Fertigungstechnik
Managementlehre und Operations Resarch
Steuerungs- und Systemtechnik

Fields of Expertise

Mobility & Production

Zugriff auf Dokument

10.1016/j.procir.2019.02.098Lizenz: CC BY-NC-ND 4.0

https://www.sciencedirect.com/science/article/pii/S221282711930215X?via%3DihubLizenz: CC BY-NC-ND 4.0

Andere Dateien und Links

http://www.scopus.com/inward/record.url?scp=85065420211&partnerID=8YFLogxK

2 Abgeschlossen

AHSM-B - Smart Instandhaltung Teil B
Furian, N.
1/06/17 → 31/07/18
Projekt: Forschungsprojekt
AHSM - Hungaria Smart Instandhaltung
Furian, N., Vorraber, W., Gutschi, C. & Neubacher, D.
1/11/15 → 30/04/17
Projekt: Forschungsprojekt

2 Vortrag bei Konferenz oder Fachtagung

Log-based predictive maintenance in discrete parts manufacturing
Clemens Gutschi (Redner/in)
2019
Aktivität: Vortrag oder Präsentation › Vortrag bei Konferenz oder Fachtagung › Science to science
Log-based predictive maintenance in discrete parts manufacturing
Clemens Gutschi (Redner/in)
18 Juli 2018
Aktivität: Vortrag oder Präsentation › Vortrag bei Konferenz oder Fachtagung › Science to science

Dieses zitieren

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title = "Log-based predictive maintenance in discrete parts manufacturing",

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 approach for estimating the probability of machine breakdown during specified time interval 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. The paper describes applied data-mining, feature-extraction and machine learning methods and concludes with results indicating that machine failures can be reliably predicted up to 168 hours in advance.",

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AU - Gutschi, Clemens

AU - Furian, Nikolaus

AU - Suschnigg, Josef

AU - Neubacher, Dietmar

AU - Vössner, Siegfried

N1 - Clemens Gutschi, Nikolaus Furian, Josef Suschnigg, Dietmar Neubacher, Siegfried Voessner, Log-based predictive maintenance in discrete parts manufacturing, Procedia CIRP, Volume 79, 2019, Pages 528-533, ISSN 2212-8271, https://doi.org/10.1016/j.procir.2019.02.098. (http://www.sciencedirect.com/science/article/pii/S221282711930215X)

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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 approach for estimating the probability of machine breakdown during specified time interval 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. The paper describes applied data-mining, feature-extraction and machine learning methods and concludes with results indicating that machine failures can be reliably predicted up to 168 hours in advance.

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 approach for estimating the probability of machine breakdown during specified time interval 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. The paper describes applied data-mining, feature-extraction and machine learning methods and concludes with results indicating that machine failures can be reliably predicted up to 168 hours in advance.

KW - Prädiktion

KW - Restlebensdauer

KW - random forest

KW - ensemble prediction

KW - Maschinelles Lernen

KW - predictive analytics

KW - Predictive maintenance

KW - random forest

KW - ensemble prediction

KW - Machine learning

KW - Probability of failure estimation

KW - maintenance

KW - Maintenance Management

KW - data mining

KW - feature engineering

KW - feature selection

KW - Remaining useful life

KW - Ensemble prediction

KW - Random forrest

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U2 - 10.1016/j.procir.2019.02.098

DO - 10.1016/j.procir.2019.02.098

M3 - Conference article

SN - 2212-8271

VL - 79

SP - 528

EP - 533

JO - Procedia CIRP

JF - Procedia CIRP

T2 - Intelligent Computation in Manufacturing Engineering

Y2 - 18 July 2018 through 20 July 2018

ER -

Log-based predictive maintenance in discrete parts manufacturing

Abstract

Schlagwörter

ASJC Scopus subject areas

Fields of Expertise

Zugriff auf Dokument

Andere Dateien und Links

Fingerprint

Projekte

AHSM-B - Smart Instandhaltung Teil B

AHSM - Hungaria Smart Instandhaltung

Aktivitäten