Comparing machine learning methods on concept drift detection for Predictive Maintenance

Jan Zenisek; Josef Wolfartsberger; Christoph Sievi; Michael Affenzeller

Comparing machine learning methods on concept drift detection for Predictive Maintenance

Jan Zenisek, Josef Wolfartsberger, Christoph Sievi, Michael Affenzeller

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In this work we present a comparison of various machine learning algorithms with the objective of detecting concept drifts in data streams characteristical for condition monitoring of industrial production plants. Although there is a fair number of contributions employing machine learning algorithms in related fields such as traditional time series forecasting or concept drift learning, data sets with sensor streams from a production plant are rarely covered. This work aims at shedding some light on the matter of how efficient the depicted algorithms perform on concept drift detection to pave the way for Predictive Maintenance (PdM) and which intermediate data processing steps therefore might be beneficial.

Original language	English
Title of host publication	30th European Modeling and Simulation Symposium, EMSS 2018
Editors	Yuri Merkuryev, Miquel Angel Piera, Francesco Longo, Agostino G. Bruzzone, Michael Affenzeller, Emilio Jimenez
Publisher	DIME UNIVERSITY OF GENOA
Pages	115-122
Number of pages	8
ISBN (Electronic)	9788885741065
ISBN (Print)	978-88-85741-03-4
Publication status	Published - 2018
Event	The 30th European Modeling & Simulation Symposium EMSS 2018 - Budapest, Hungary, Hungary Duration: 17 Sept 2018 → 19 Sept 2018 http://www.msc-les.org/conf/emss2018/index.html

Publication series

Name	30th European Modeling and Simulation Symposium, EMSS 2018

Conference

Conference	The 30th European Modeling & Simulation Symposium EMSS 2018
Country/Territory	Hungary
City	Budapest, Hungary
Period	17.09.2018 → 19.09.2018
Internet address	http://www.msc-les.org/conf/emss2018/index.html

Keywords

Machine Learning
Predictive Maintenance
Concept Drift Detection
Time Series Regression
Machine learning
Time series regression
Concept drift detection
Predictive maintenance

Cite this

Zenisek, J., Wolfartsberger, J., Sievi, C., & Affenzeller, M. (2018). Comparing machine learning methods on concept drift detection for Predictive Maintenance. In Y. Merkuryev, M. A. Piera, F. Longo, A. G. Bruzzone, M. Affenzeller, & E. Jimenez (Eds.), 30th European Modeling and Simulation Symposium, EMSS 2018 (pp. 115-122). (30th European Modeling and Simulation Symposium, EMSS 2018). DIME UNIVERSITY OF GENOA.

Zenisek, Jan ; Wolfartsberger, Josef ; Sievi, Christoph et al. / Comparing machine learning methods on concept drift detection for Predictive Maintenance. 30th European Modeling and Simulation Symposium, EMSS 2018. editor / Yuri Merkuryev ; Miquel Angel Piera ; Francesco Longo ; Agostino G. Bruzzone ; Michael Affenzeller ; Emilio Jimenez. DIME UNIVERSITY OF GENOA, 2018. pp. 115-122 (30th European Modeling and Simulation Symposium, EMSS 2018).

@inproceedings{09c69a40131c4dcdb1f6f210ed9283aa,

title = "Comparing machine learning methods on concept drift detection for Predictive Maintenance",

abstract = "In this work we present a comparison of various machine learning algorithms with the objective of detecting concept drifts in data streams characteristical for condition monitoring of industrial production plants. Although there is a fair number of contributions employing machine learning algorithms in related fields such as traditional time series forecasting or concept drift learning, data sets with sensor streams from a production plant are rarely covered. This work aims at shedding some light on the matter of how efficient the depicted algorithms perform on concept drift detection to pave the way for Predictive Maintenance (PdM) and which intermediate data processing steps therefore might be beneficial.",

keywords = "Machine Learning, Predictive Maintenance, Concept Drift Detection, Time Series Regression, Machine Learning, Predictive Maintenance, Concept Drift Detection, Time Series Regression, Machine learning, Time series regression, Concept drift detection, Predictive maintenance",

author = "Jan Zenisek and Josef Wolfartsberger and Christoph Sievi and Michael Affenzeller",

year = "2018",

language = "English",

isbn = "978-88-85741-03-4",

series = "30th European Modeling and Simulation Symposium, EMSS 2018",

publisher = "DIME UNIVERSITY OF GENOA",

pages = "115--122",

editor = "Yuri Merkuryev and Piera, {Miquel Angel} and Francesco Longo and Bruzzone, {Agostino G.} and Michael Affenzeller and Emilio Jimenez",

booktitle = "30th European Modeling and Simulation Symposium, EMSS 2018",

note = "The 30th European Modeling & Simulation Symposium EMSS 2018 ; Conference date: 17-09-2018 Through 19-09-2018",

url = "http://www.msc-les.org/conf/emss2018/index.html",

}

Zenisek, J , Wolfartsberger, J, Sievi, C & Affenzeller, M 2018, Comparing machine learning methods on concept drift detection for Predictive Maintenance. in Y Merkuryev, MA Piera, F Longo, AG Bruzzone, M Affenzeller & E Jimenez (eds), 30th European Modeling and Simulation Symposium, EMSS 2018. 30th European Modeling and Simulation Symposium, EMSS 2018, DIME UNIVERSITY OF GENOA, pp. 115-122, The 30th European Modeling & Simulation Symposium EMSS 2018, Budapest, Hungary, Hungary, 17.09.2018.

Comparing machine learning methods on concept drift detection for Predictive Maintenance. / Zenisek, Jan ; Wolfartsberger, Josef; Sievi, Christoph et al.
30th European Modeling and Simulation Symposium, EMSS 2018. ed. / Yuri Merkuryev; Miquel Angel Piera; Francesco Longo; Agostino G. Bruzzone; Michael Affenzeller; Emilio Jimenez. DIME UNIVERSITY OF GENOA, 2018. p. 115-122 (30th European Modeling and Simulation Symposium, EMSS 2018).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

TY - GEN

T1 - Comparing machine learning methods on concept drift detection for Predictive Maintenance

AU - Zenisek, Jan

AU - Wolfartsberger, Josef

AU - Sievi, Christoph

AU - Affenzeller, Michael

PY - 2018

Y1 - 2018

N2 - In this work we present a comparison of various machine learning algorithms with the objective of detecting concept drifts in data streams characteristical for condition monitoring of industrial production plants. Although there is a fair number of contributions employing machine learning algorithms in related fields such as traditional time series forecasting or concept drift learning, data sets with sensor streams from a production plant are rarely covered. This work aims at shedding some light on the matter of how efficient the depicted algorithms perform on concept drift detection to pave the way for Predictive Maintenance (PdM) and which intermediate data processing steps therefore might be beneficial.

AB - In this work we present a comparison of various machine learning algorithms with the objective of detecting concept drifts in data streams characteristical for condition monitoring of industrial production plants. Although there is a fair number of contributions employing machine learning algorithms in related fields such as traditional time series forecasting or concept drift learning, data sets with sensor streams from a production plant are rarely covered. This work aims at shedding some light on the matter of how efficient the depicted algorithms perform on concept drift detection to pave the way for Predictive Maintenance (PdM) and which intermediate data processing steps therefore might be beneficial.

KW - Machine Learning

KW - Predictive Maintenance

KW - Concept Drift Detection

KW - Time Series Regression

KW - Machine Learning

KW - Predictive Maintenance

KW - Concept Drift Detection

KW - Time Series Regression

KW - Machine learning

KW - Time series regression

KW - Concept drift detection

KW - Predictive maintenance

M3 - Conference contribution

SN - 978-88-85741-03-4

T3 - 30th European Modeling and Simulation Symposium, EMSS 2018

SP - 115

EP - 122

BT - 30th European Modeling and Simulation Symposium, EMSS 2018

A2 - Merkuryev, Yuri

A2 - Piera, Miquel Angel

A2 - Longo, Francesco

A2 - Bruzzone, Agostino G.

A2 - Affenzeller, Michael

A2 - Jimenez, Emilio

PB - DIME UNIVERSITY OF GENOA

T2 - The 30th European Modeling & Simulation Symposium EMSS 2018

Y2 - 17 September 2018 through 19 September 2018

ER -

Zenisek J , Wolfartsberger J, Sievi C, Affenzeller M. Comparing machine learning methods on concept drift detection for Predictive Maintenance. In Merkuryev Y, Piera MA, Longo F, Bruzzone AG, Affenzeller M, Jimenez E, editors, 30th European Modeling and Simulation Symposium, EMSS 2018. DIME UNIVERSITY OF GENOA. 2018. p. 115-122. (30th European Modeling and Simulation Symposium, EMSS 2018).

Comparing machine learning methods on concept drift detection for Predictive Maintenance

Abstract

Publication series

Conference

Keywords

Fingerprint

Cite this