TY - GEN
T1 - Machine level energy data analysis - Development and validation of a machine learning based tool
AU - Carvalho, Samuel
AU - Cosgrove, John
AU - Rezende, Julio
AU - Doyle, Frank
N1 - Publisher Copyright:
© 2018 eceee and the authors, Stockholm.
PY - 2018
Y1 - 2018
N2 - Industrial energy consumption is known to be significantly high worldwide, reaching up to one half of the total energy in some countries and almost one third of the world's consumed energy. Consequently, the industrial sector is also responsible for a large share of greenhouse gases (GHG) emissions. Many recent manufacturing standards and methodologies have efficiency, environmental and social impacts as key aspects and concerns, in order to meet the demands of international agreements and regulations on these subjects. The ongoing development brought by Industry 4.0 and Smart Factories are the main demonstrations of the growing awareness of the importance of efficient and intelligent production. Data gathering, processing, analysis and benchmarking play a key role in this scenario, enabling a smarter and well informed decision-making process. In this context, energy consumption data extracted directly at machine level inside factories also show a significant potential for being a reliable source of process-related information, such as automatic production counting, consumption analysis, Overall Equipment Effectiveness (OEE) and costs identification. This paper presents a pilot implementation of a machine learning based application for the automatic extraction of useful insights from machine level energy data sets. The developed tool uses a K-means clustering algorithm in order to categorise energy profiles into production or idle periods, from which relevant Key Performance Indicators (KPIs) are calculated. A controlled experiment using Non-Invasive Load Monitoring (NILM) equipment connected to a CNC milling machine was performed in order to validate the approach, and the results are presented in this paper. The method has identified potential savings of up to 30 % in an Injection Moulding machine and up to 25 % in a Precision Engineering company, besides providing deeper understanding of the consumption profiles of machines in these sectors.
AB - Industrial energy consumption is known to be significantly high worldwide, reaching up to one half of the total energy in some countries and almost one third of the world's consumed energy. Consequently, the industrial sector is also responsible for a large share of greenhouse gases (GHG) emissions. Many recent manufacturing standards and methodologies have efficiency, environmental and social impacts as key aspects and concerns, in order to meet the demands of international agreements and regulations on these subjects. The ongoing development brought by Industry 4.0 and Smart Factories are the main demonstrations of the growing awareness of the importance of efficient and intelligent production. Data gathering, processing, analysis and benchmarking play a key role in this scenario, enabling a smarter and well informed decision-making process. In this context, energy consumption data extracted directly at machine level inside factories also show a significant potential for being a reliable source of process-related information, such as automatic production counting, consumption analysis, Overall Equipment Effectiveness (OEE) and costs identification. This paper presents a pilot implementation of a machine learning based application for the automatic extraction of useful insights from machine level energy data sets. The developed tool uses a K-means clustering algorithm in order to categorise energy profiles into production or idle periods, from which relevant Key Performance Indicators (KPIs) are calculated. A controlled experiment using Non-Invasive Load Monitoring (NILM) equipment connected to a CNC milling machine was performed in order to validate the approach, and the results are presented in this paper. The method has identified potential savings of up to 30 % in an Injection Moulding machine and up to 25 % in a Precision Engineering company, besides providing deeper understanding of the consumption profiles of machines in these sectors.
KW - Data monitoring
KW - Energy profiles
KW - Industrial energy saving
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85049922630&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85049922630
SN - 9789198387827
T3 - Eceee Industrial Summer Study Proceedings
SP - 477
EP - 486
BT - ECEEE Industrial Summer Study on Industrial Efficiency 2018
PB - European Council for an Energy Efficient Economy
T2 - 2018 ECEEE Industrial Summer Study on Industrial Efficiency: Leading the Low-Carbon Transition
Y2 - 11 June 2018 through 13 June 2018
ER -
