Data-Driven Smart Manufacturing Technologies for Prop Shop Systems

Zhicheng Xu; Weinan Gao; Zhicun Chen; Rami Haddad; Scot Hudson; Ezebuugo Nwaonumah; Frank Zahiri; Jeremy Johnson

doi:10.1109/SERA57763.2023.10197769

Data-Driven Smart Manufacturing Technologies for Prop Shop Systems

Zhicheng Xu
, Weinan Gao
, Zhicun Chen
, Rami Haddad
, Scot Hudson
, Ezebuugo Nwaonumah
, Frank Zahiri
, Jeremy Johnson

Electrical & Computer Engineering

Research output: Contribution to book or proceeding › Conference article › peer-review

Abstract

In this paper, a data-driven framework was designed to predict manufacturing failure. The framework includes an autoregression model with the least mean square algorithm, a linear regression model with prediction intervals for short-Term and long-Term failure detection, and a feature extraction model with empirical mode decomposition. The analytical results validate that the designed data-driven model is a good candidate for failure predictions in smart manufacturing processes.

Original language	English
Title of host publication	Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023
Editors	Yeong-Tae Song, Junghwan Rhee, Yuseok Jeon
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	189-194
Number of pages	6
ISBN (Electronic)	9798350345889
ISBN (Print)	9798350345889
DOIs	https://doi.org/10.1109/SERA57763.2023.10197769
State	Published - May 23 2023
Event	21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023 - Orlando, United States Duration: May 23 2023 → May 25 2023

Publication series

Name	2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)

Conference

Conference	21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023
Country/Territory	United States
City	Orlando
Period	05/23/23 → 05/25/23

UN SDGs

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

SDG 7 Affordable and Clean Energy

Scopus Subject Areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering
Safety, Risk, Reliability and Quality
Environmental Engineering

Keywords

Autoregression Model
Diagnostic and Prognostic Algorithms
Empirical Mode Decomposition
Machine Learning

Access to Document

10.1109/SERA57763.2023.10197769

Cite this

Xu, Z., Gao, W., Chen, Z., Haddad, R., Hudson, S., Nwaonumah, E., Zahiri, F., & Johnson, J. (2023). Data-Driven Smart Manufacturing Technologies for Prop Shop Systems. In Y.-T. Song, J. Rhee, & Y. Jeon (Eds.), Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023 (pp. 189-194). (2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SERA57763.2023.10197769

Xu, Zhicheng ; Gao, Weinan ; Chen, Zhicun et al. / Data-Driven Smart Manufacturing Technologies for Prop Shop Systems. Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023. editor / Yeong-Tae Song ; Junghwan Rhee ; Yuseok Jeon. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 189-194 (2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)).

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title = "Data-Driven Smart Manufacturing Technologies for Prop Shop Systems",

abstract = "In this paper, a data-driven framework was designed to predict manufacturing failure. The framework includes an autoregression model with the least mean square algorithm, a linear regression model with prediction intervals for short-Term and long-Term failure detection, and a feature extraction model with empirical mode decomposition. The analytical results validate that the designed data-driven model is a good candidate for failure predictions in smart manufacturing processes.",

keywords = "Autoregression Model, Diagnostic and Prognostic Algorithms, Empirical Mode Decomposition, Machine Learning",

author = "Zhicheng Xu and Weinan Gao and Zhicun Chen and Rami Haddad and Scot Hudson and Ezebuugo Nwaonumah and Frank Zahiri and Jeremy Johnson",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023 ; Conference date: 23-05-2023 Through 25-05-2023",

year = "2023",

month = may,

day = "23",

doi = "10.1109/SERA57763.2023.10197769",

language = "English",

isbn = "9798350345889",

series = "2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "189--194",

editor = "Yeong-Tae Song and Junghwan Rhee and Yuseok Jeon",

booktitle = "Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023",

address = "United States",

}

Xu, Z, Gao, W, Chen, Z, Haddad, R, Hudson, S, Nwaonumah, E, Zahiri, F & Johnson, J 2023, Data-Driven Smart Manufacturing Technologies for Prop Shop Systems. in Y-T Song, J Rhee & Y Jeon (eds), Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023. 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA), Institute of Electrical and Electronics Engineers Inc., pp. 189-194, 21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023, Orlando, United States, 05/23/23. https://doi.org/10.1109/SERA57763.2023.10197769

Data-Driven Smart Manufacturing Technologies for Prop Shop Systems. / Xu, Zhicheng; Gao, Weinan; Chen, Zhicun et al.
Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023. ed. / Yeong-Tae Song; Junghwan Rhee; Yuseok Jeon. Institute of Electrical and Electronics Engineers Inc., 2023. p. 189-194 (2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - Data-Driven Smart Manufacturing Technologies for Prop Shop Systems

AU - Xu, Zhicheng

AU - Gao, Weinan

AU - Chen, Zhicun

AU - Haddad, Rami

AU - Hudson, Scot

AU - Nwaonumah, Ezebuugo

AU - Zahiri, Frank

AU - Johnson, Jeremy

PY - 2023/5/23

Y1 - 2023/5/23

N2 - In this paper, a data-driven framework was designed to predict manufacturing failure. The framework includes an autoregression model with the least mean square algorithm, a linear regression model with prediction intervals for short-Term and long-Term failure detection, and a feature extraction model with empirical mode decomposition. The analytical results validate that the designed data-driven model is a good candidate for failure predictions in smart manufacturing processes.

AB - In this paper, a data-driven framework was designed to predict manufacturing failure. The framework includes an autoregression model with the least mean square algorithm, a linear regression model with prediction intervals for short-Term and long-Term failure detection, and a feature extraction model with empirical mode decomposition. The analytical results validate that the designed data-driven model is a good candidate for failure predictions in smart manufacturing processes.

KW - Autoregression Model

KW - Diagnostic and Prognostic Algorithms

KW - Empirical Mode Decomposition

KW - Machine Learning

UR - https://www.scopus.com/pages/publications/85168760809

U2 - 10.1109/SERA57763.2023.10197769

DO - 10.1109/SERA57763.2023.10197769

M3 - Conference article

AN - SCOPUS:85168760809

SN - 9798350345889

T3 - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)

SP - 189

EP - 194

BT - Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023

A2 - Song, Yeong-Tae

A2 - Rhee, Junghwan

A2 - Jeon, Yuseok

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023

Y2 - 23 May 2023 through 25 May 2023

ER -

Xu Z, Gao W, Chen Z, Haddad R, Hudson S, Nwaonumah E et al. Data-Driven Smart Manufacturing Technologies for Prop Shop Systems. In Song YT, Rhee J, Jeon Y, editors, Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 189-194. (2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)). doi: 10.1109/SERA57763.2023.10197769

Data-Driven Smart Manufacturing Technologies for Prop Shop Systems

Abstract

Publication series

Conference

UN SDGs

Scopus Subject Areas

Keywords

Access to Document

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