A practical energy modeling method for industrial robots in manufacturing

Wenjun Xu; Huan Liu; Jiayi Liu; Zude Zhou; Duc Truong Pham

doi:10.1007/978-3-319-61994-1_3

A practical energy modeling method for industrial robots in manufacturing

Wenjun Xu^*
, Huan Liu
, Jiayi Liu
, Zude Zhou
, Duc Truong Pham

^*Corresponding author for this work

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Industrial robots (IRs) are widely used in modern manufacturing systems, and energy problem of IRs is paid more attention to meet requirements of environment protection. Therefore, it is necessary to investigate the approaches to optimize the energy consumption of IRs, and the energy consumption model is the basis for enabling such approaches. Usually, energy consumption modeling for IRs is based on dynamic parameters identification. Meanwhile, the physical parameters, e.g. angle, velocity, acceleration, torque, etc. are all the necessary data of parameter identification. However, since the parts of IRs are not easy to be disassembled and the sensor modules can not be installed easily inside IRs, it is difficult to obtain all such physical parameters through sensing method, in particular the torque data. In this context, a practical energy modeling method by measuring total power for IRs is proposed. This method avoids the problem of directly measuring relevant parameters inside IRs, and the parameter identification process is gradually carried out by several excitation experiments. The experimental results show that the proposed energy modeling method can be used to predict the energy consumption of the process used in robot movement in manufacturing processes, and it can also efficiently support the analysis of the energy consumption characteristics of IRs.

Original language	English
Title of host publication	Challenges and Opportunity with Big Data
Subtitle of host publication	19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016 Revised Selected Papers
Editors	Lin Zhang, Lei Ren, Fabrice Kordon
Publisher	Springer Verlag
Pages	25-36
Number of pages	12
ISBN (Print)	9783319619934
DOIs	https://doi.org/10.1007/978-3-319-61994-1_3
Publication status	Published - 1 Jan 2017
Event	19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016 - Beijing, China Duration: 8 Oct 2016 → 11 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10228 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016
Country/Territory	China
City	Beijing
Period	8/10/16 → 11/10/16

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Energy consumption
Energy modeling
Industrial robots
Power measurement

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-61994-1_3Licence: None: All rights reserved

https://link.springer.com/chapter/10.1007%2F978-3-319-61994-1_3Licence: None: All rights reserved

Cite this

Xu, W., Liu, H., Liu, J., Zhou, Z., & Pham, D. T. (2017). A practical energy modeling method for industrial robots in manufacturing. In L. Zhang, L. Ren, & F. Kordon (Eds.), Challenges and Opportunity with Big Data: 19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016 Revised Selected Papers (pp. 25-36). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10228 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61994-1_3

Xu, Wenjun ; Liu, Huan ; Liu, Jiayi et al. / A practical energy modeling method for industrial robots in manufacturing. Challenges and Opportunity with Big Data: 19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016 Revised Selected Papers. editor / Lin Zhang ; Lei Ren ; Fabrice Kordon. Springer Verlag, 2017. pp. 25-36 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0df665686f804f1a8aa118e29aa40763,

title = "A practical energy modeling method for industrial robots in manufacturing",

abstract = "Industrial robots (IRs) are widely used in modern manufacturing systems, and energy problem of IRs is paid more attention to meet requirements of environment protection. Therefore, it is necessary to investigate the approaches to optimize the energy consumption of IRs, and the energy consumption model is the basis for enabling such approaches. Usually, energy consumption modeling for IRs is based on dynamic parameters identification. Meanwhile, the physical parameters, e.g. angle, velocity, acceleration, torque, etc. are all the necessary data of parameter identification. However, since the parts of IRs are not easy to be disassembled and the sensor modules can not be installed easily inside IRs, it is difficult to obtain all such physical parameters through sensing method, in particular the torque data. In this context, a practical energy modeling method by measuring total power for IRs is proposed. This method avoids the problem of directly measuring relevant parameters inside IRs, and the parameter identification process is gradually carried out by several excitation experiments. The experimental results show that the proposed energy modeling method can be used to predict the energy consumption of the process used in robot movement in manufacturing processes, and it can also efficiently support the analysis of the energy consumption characteristics of IRs.",

keywords = "Energy consumption, Energy modeling, Industrial robots, Power measurement",

author = "Wenjun Xu and Huan Liu and Jiayi Liu and Zude Zhou and Pham, \{Duc Truong\}",

year = "2017",

month = jan,

day = "1",

doi = "10.1007/978-3-319-61994-1\_3",

language = "English",

isbn = "9783319619934",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "25--36",

editor = "Zhang, \{Lin \} and Ren, \{Lei \} and Kordon, \{Fabrice \}",

booktitle = "Challenges and Opportunity with Big Data",

note = "19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016 ; Conference date: 08-10-2016 Through 11-10-2016",

}

Xu, W, Liu, H, Liu, J, Zhou, Z & Pham, DT 2017, A practical energy modeling method for industrial robots in manufacturing. in L Zhang, L Ren & F Kordon (eds), Challenges and Opportunity with Big Data: 19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016 Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10228 LNCS, Springer Verlag, pp. 25-36, 19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016, Beijing, China, 8/10/16. https://doi.org/10.1007/978-3-319-61994-1_3

A practical energy modeling method for industrial robots in manufacturing. / Xu, Wenjun; Liu, Huan; Liu, Jiayi et al.
Challenges and Opportunity with Big Data: 19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016 Revised Selected Papers. ed. / Lin Zhang; Lei Ren; Fabrice Kordon. Springer Verlag, 2017. p. 25-36 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10228 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A practical energy modeling method for industrial robots in manufacturing

AU - Xu, Wenjun

AU - Liu, Huan

AU - Liu, Jiayi

AU - Zhou, Zude

AU - Pham, Duc Truong

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Industrial robots (IRs) are widely used in modern manufacturing systems, and energy problem of IRs is paid more attention to meet requirements of environment protection. Therefore, it is necessary to investigate the approaches to optimize the energy consumption of IRs, and the energy consumption model is the basis for enabling such approaches. Usually, energy consumption modeling for IRs is based on dynamic parameters identification. Meanwhile, the physical parameters, e.g. angle, velocity, acceleration, torque, etc. are all the necessary data of parameter identification. However, since the parts of IRs are not easy to be disassembled and the sensor modules can not be installed easily inside IRs, it is difficult to obtain all such physical parameters through sensing method, in particular the torque data. In this context, a practical energy modeling method by measuring total power for IRs is proposed. This method avoids the problem of directly measuring relevant parameters inside IRs, and the parameter identification process is gradually carried out by several excitation experiments. The experimental results show that the proposed energy modeling method can be used to predict the energy consumption of the process used in robot movement in manufacturing processes, and it can also efficiently support the analysis of the energy consumption characteristics of IRs.

AB - Industrial robots (IRs) are widely used in modern manufacturing systems, and energy problem of IRs is paid more attention to meet requirements of environment protection. Therefore, it is necessary to investigate the approaches to optimize the energy consumption of IRs, and the energy consumption model is the basis for enabling such approaches. Usually, energy consumption modeling for IRs is based on dynamic parameters identification. Meanwhile, the physical parameters, e.g. angle, velocity, acceleration, torque, etc. are all the necessary data of parameter identification. However, since the parts of IRs are not easy to be disassembled and the sensor modules can not be installed easily inside IRs, it is difficult to obtain all such physical parameters through sensing method, in particular the torque data. In this context, a practical energy modeling method by measuring total power for IRs is proposed. This method avoids the problem of directly measuring relevant parameters inside IRs, and the parameter identification process is gradually carried out by several excitation experiments. The experimental results show that the proposed energy modeling method can be used to predict the energy consumption of the process used in robot movement in manufacturing processes, and it can also efficiently support the analysis of the energy consumption characteristics of IRs.

KW - Energy consumption

KW - Energy modeling

KW - Industrial robots

KW - Power measurement

UR - https://www.scopus.com/pages/publications/85028577860

U2 - 10.1007/978-3-319-61994-1_3

DO - 10.1007/978-3-319-61994-1_3

M3 - Conference contribution

AN - SCOPUS:85028577860

SN - 9783319619934

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 25

EP - 36

BT - Challenges and Opportunity with Big Data

A2 - Zhang, Lin

A2 - Ren, Lei

A2 - Kordon, Fabrice

PB - Springer Verlag

T2 - 19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016

Y2 - 8 October 2016 through 11 October 2016

ER -

Xu W, Liu H, Liu J, Zhou Z, Pham DT. A practical energy modeling method for industrial robots in manufacturing. In Zhang L, Ren L, Kordon F, editors, Challenges and Opportunity with Big Data: 19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016 Revised Selected Papers. Springer Verlag. 2017. p. 25-36. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61994-1_3

A practical energy modeling method for industrial robots in manufacturing

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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