Advanced energy data analytics to predict machine overall equipment effectiveness (OEE): a synergetic approach to foster sustainable manufacturing

Sebastian Thiede

doi:10.1016/j.procir.2023.02.074

Advanced energy data analytics to predict machine overall equipment effectiveness (OEE): a synergetic approach to foster sustainable manufacturing

Sebastian Thiede^*

^*Corresponding author for this work

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

13 Citations (Scopus)

299 Downloads (Pure)

Abstract

Understanding the relation of production activities and energy demand is of crucial importance for fostering sustainable manufacturing. While so far the perspective is mainly on augmenting production data with energy related aspects, this paper suggests an alternative approach to overcome current challenges. Energy data is the starting point and shall be utilized for the prediction of the overall equipment effectiveness (OEE) which is an established and comprehensive indicator for machine performance. Based on a common underlying definitory framework, two alternative prediction methods are presented. Results indicate that an energy based OEE prediction is actually possible with reasonable accuracy and effort.

Original language	English
Pages (from-to)	438-443
Number of pages	6
Journal	Procedia CIRP
Volume	116
Early online date	18 Apr 2023
DOIs	https://doi.org/10.1016/j.procir.2023.02.074
Publication status	Published - 2023
Event	30th CIRP Life Cycle Engineering Conference, LCE 2023 - Rutgers Academic Building, New Brunswick, United States Duration: 15 May 2023 → 17 May 2023 Conference number: 30

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production

Keywords

Energy efficiency
Machine
Overall Equipment Effectiveness (OEE)

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10.1016/j.procir.2023.02.074Licence: CC BY-NC-ND

Advanced energy data analyticsFinal published version, 820 KBLicence: CC BY-NC-ND

Cite this

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title = "Advanced energy data analytics to predict machine overall equipment effectiveness (OEE): a synergetic approach to foster sustainable manufacturing",

abstract = "Understanding the relation of production activities and energy demand is of crucial importance for fostering sustainable manufacturing. While so far the perspective is mainly on augmenting production data with energy related aspects, this paper suggests an alternative approach to overcome current challenges. Energy data is the starting point and shall be utilized for the prediction of the overall equipment effectiveness (OEE) which is an established and comprehensive indicator for machine performance. Based on a common underlying definitory framework, two alternative prediction methods are presented. Results indicate that an energy based OEE prediction is actually possible with reasonable accuracy and effort.",

keywords = "Energy efficiency, Machine, Overall Equipment Effectiveness (OEE)",

author = "Sebastian Thiede",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors. Published by Elsevier B.V.; 30th CIRP Life Cycle Engineering Conference, LCE 2023, LCE 2023 ; Conference date: 15-05-2023 Through 17-05-2023",

year = "2023",

doi = "10.1016/j.procir.2023.02.074",

language = "English",

volume = "116",

pages = "438--443",

journal = "Procedia CIRP",

issn = "2212-8271",

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TY - JOUR

T1 - Advanced energy data analytics to predict machine overall equipment effectiveness (OEE)

T2 - 30th CIRP Life Cycle Engineering Conference, LCE 2023

AU - Thiede, Sebastian

N1 - Conference code: 30

PY - 2023

Y1 - 2023

N2 - Understanding the relation of production activities and energy demand is of crucial importance for fostering sustainable manufacturing. While so far the perspective is mainly on augmenting production data with energy related aspects, this paper suggests an alternative approach to overcome current challenges. Energy data is the starting point and shall be utilized for the prediction of the overall equipment effectiveness (OEE) which is an established and comprehensive indicator for machine performance. Based on a common underlying definitory framework, two alternative prediction methods are presented. Results indicate that an energy based OEE prediction is actually possible with reasonable accuracy and effort.

AB - Understanding the relation of production activities and energy demand is of crucial importance for fostering sustainable manufacturing. While so far the perspective is mainly on augmenting production data with energy related aspects, this paper suggests an alternative approach to overcome current challenges. Energy data is the starting point and shall be utilized for the prediction of the overall equipment effectiveness (OEE) which is an established and comprehensive indicator for machine performance. Based on a common underlying definitory framework, two alternative prediction methods are presented. Results indicate that an energy based OEE prediction is actually possible with reasonable accuracy and effort.

KW - Energy efficiency

KW - Machine

KW - Overall Equipment Effectiveness (OEE)

UR - https://www.scopus.com/pages/publications/85164278076

U2 - 10.1016/j.procir.2023.02.074

DO - 10.1016/j.procir.2023.02.074

M3 - Conference article

AN - SCOPUS:85164278076

SN - 2212-8271

VL - 116

SP - 438

EP - 443

JO - Procedia CIRP

JF - Procedia CIRP

Y2 - 15 May 2023 through 17 May 2023

ER -

Advanced energy data analytics to predict machine overall equipment effectiveness (OEE): a synergetic approach to foster sustainable manufacturing

Abstract

UN SDGs

Keywords

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