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

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Pages (from-to)	528-533
Number of pages	6
Journal	Procedia CIRP
Volume	79
DOIs	https://doi.org/10.1016/j.procir.2019.02.098
Publication status	Published - 2019
Event	Intelligent Computation in Manufacturing Engineering: Innovative and Cognitive Production Technology and Systems - Island of Ischia, Ischia, Italy Duration: 18 Jul 2018 → 20 Jul 2018 http://www.icme.unina.it/ICME%2018/ICME_14.htm

Keywords

predictive analytics
Predictive maintenance
random forest
ensemble prediction
Machine learning
Probability of failure estimation
maintenance
Maintenance Management
data mining
feature engineering
feature selection
Remaining useful life
Ensemble prediction
Random forrest

ASJC Scopus subject areas

Artificial Intelligence
Industrial and Manufacturing Engineering
Management Science and Operations Research
Control and Systems Engineering

Fields of Expertise

Mobility & Production

Access to Document

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

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

Cite this

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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.",

keywords = "Pr{\"a}diktion, Restlebensdauer, random forest, ensemble prediction, Maschinelles Lernen, predictive analytics, Predictive maintenance, random forest, ensemble prediction, Machine learning, Probability of failure estimation, maintenance, Maintenance Management, data mining, feature engineering, feature selection, Remaining useful life, Ensemble prediction, Random forrest",

author = "Clemens Gutschi and Nikolaus Furian and Josef Suschnigg and Dietmar Neubacher and Siegfried V{\"o}ssner",

note = "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); Intelligent Computation in Manufacturing Engineering : Innovative and Cognitive Production Technology and Systems, CIRP-ICME'18 ; Conference date: 18-07-2018 Through 20-07-2018",

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doi = "10.1016/j.procir.2019.02.098",

language = "English",

volume = "79",

pages = "528--533",

journal = "Procedia CIRP",

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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)

PY - 2019

Y1 - 2019

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

Keywords

ASJC Scopus subject areas

Fields of Expertise

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