Energy Consumption Data Based Machine Anomaly Detection

Hui Chen; Xiang Fei; Sheng Wang; Xin Lu; Guoqin Jin; Weidong Li; X Wu

doi:10.1109/CBD.2014.24

Energy Consumption Data Based Machine Anomaly Detection

Hui Chen, Xiang Fei, Sheng Wang, Xin Lu, Guoqin Jin, Weidong Li, X Wu

Research output: Contribution to conference › Paper

11 Citations (Scopus)

132 Downloads (Pure)

Abstract

The ever increasing of product development and the scarcity of the energy resources that those manufacturing activities heavily rely on have made it of great significance the study on how to improve the energy efficiency in manufacturing environment. Energy consumption sensing and collection enables the development of effective solutions to higher energy efficiency. Further, it is found that the data on energy consumption of manufacturing machines also contains the information on the conditions of these machines. In this paper, methods of machine anomaly detection based on energy consumption information are developed and applied to cases on our Syil X4 computer numerical control (CNC) milling machine. Further, given massive amount of energy consumption data from large amount machining tasks, the proposed algorithms are being implemented on a Storm and Hadoop based framework aiming at online realtime machine anomaly detection.

Original language	English
Pages	136 - 142
DOIs	https://doi.org/10.1109/CBD.2014.24
Publication status	Published - Nov 2014
Event	Second International Conference on Advanced Cloud and Big Data (CBD 2014) - Huangshan, China Duration: 20 Nov 2014 → 22 Nov 2014

Conference

Conference	Second International Conference on Advanced Cloud and Big Data (CBD 2014)
Country/Territory	China
City	Huangshan
Period	20/11/14 → 22/11/14

Bibliographical note

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

EU Smarter project EU CASES project

Keywords

anomaly detection
articifial neurak network
Hadoopm
Storm

UN SDGs

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

Access to Document

10.1109/CBD.2014.24

Lu 20141120 PostP

Xiang Fei
- School of Computing, Mathematics and Data Sciences - Assistant Professor Academic
Person: Teaching and Research

Cite this

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title = "Energy Consumption Data Based Machine Anomaly Detection",

abstract = "The ever increasing of product development and the scarcity of the energy resources that those manufacturing activities heavily rely on have made it of great significance the study on how to improve the energy efficiency in manufacturing environment. Energy consumption sensing and collection enables the development of effective solutions to higher energy efficiency. Further, it is found that the data on energy consumption of manufacturing machines also contains the information on the conditions of these machines. In this paper, methods of machine anomaly detection based on energy consumption information are developed and applied to cases on our Syil X4 computer numerical control (CNC) milling machine. Further, given massive amount of energy consumption data from large amount machining tasks, the proposed algorithms are being implemented on a Storm and Hadoop based framework aiming at online realtime machine anomaly detection. ",

keywords = "anomaly detection, articifial neurak network, Hadoopm, Storm",

author = "Hui Chen and Xiang Fei and Sheng Wang and Xin Lu and Guoqin Jin and Weidong Li and X Wu",

note = "{\textcopyright} 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. EU Smarter project EU CASES project; Second International Conference on Advanced Cloud and Big Data (CBD 2014) ; Conference date: 20-11-2014 Through 22-11-2014",

year = "2014",

month = nov,

doi = "10.1109/CBD.2014.24",

language = "English",

pages = "136 -- 142",

}

TY - CONF

T1 - Energy Consumption Data Based Machine Anomaly Detection

AU - Chen, Hui

AU - Fei, Xiang

AU - Wang, Sheng

AU - Lu, Xin

AU - Jin, Guoqin

AU - Li, Weidong

AU - Wu, X

N1 - © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. EU Smarter project EU CASES project

PY - 2014/11

Y1 - 2014/11

N2 - The ever increasing of product development and the scarcity of the energy resources that those manufacturing activities heavily rely on have made it of great significance the study on how to improve the energy efficiency in manufacturing environment. Energy consumption sensing and collection enables the development of effective solutions to higher energy efficiency. Further, it is found that the data on energy consumption of manufacturing machines also contains the information on the conditions of these machines. In this paper, methods of machine anomaly detection based on energy consumption information are developed and applied to cases on our Syil X4 computer numerical control (CNC) milling machine. Further, given massive amount of energy consumption data from large amount machining tasks, the proposed algorithms are being implemented on a Storm and Hadoop based framework aiming at online realtime machine anomaly detection.

AB - The ever increasing of product development and the scarcity of the energy resources that those manufacturing activities heavily rely on have made it of great significance the study on how to improve the energy efficiency in manufacturing environment. Energy consumption sensing and collection enables the development of effective solutions to higher energy efficiency. Further, it is found that the data on energy consumption of manufacturing machines also contains the information on the conditions of these machines. In this paper, methods of machine anomaly detection based on energy consumption information are developed and applied to cases on our Syil X4 computer numerical control (CNC) milling machine. Further, given massive amount of energy consumption data from large amount machining tasks, the proposed algorithms are being implemented on a Storm and Hadoop based framework aiming at online realtime machine anomaly detection.

KW - anomaly detection

KW - articifial neurak network

KW - Hadoopm

KW - Storm

U2 - 10.1109/CBD.2014.24

DO - 10.1109/CBD.2014.24

M3 - Paper

SP - 136

EP - 142

T2 - Second International Conference on Advanced Cloud and Big Data (CBD 2014)

Y2 - 20 November 2014 through 22 November 2014

ER -

Energy Consumption Data Based Machine Anomaly Detection

Abstract

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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Profiles

Xiang Fei

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