Electrical & Computer Engineering: Faculty Publications
Data-Driven Smart Manufacturing Technologies for Prop Shop Systems
Document Type
Article
Publication Date
5-23-2023
Publication Title
Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023
DOI
10.1109/SERA57763.2023.10197769
ISBN
9798350345889
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.
Recommended Citation
Xu, Zhicheng, Weinan Gao, Zhicun Chen, Rami J. Haddad, Scot Hudson, Ezebuugo Nwaonumah, Frank Zahiri, Jeremy Johnson.
2023.
"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: 189-194.
doi: 10.1109/SERA57763.2023.10197769 isbn: 9798350345889
https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/183
Copyright
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Comments
Georgia Southern University faculty members, Rami Haddad co-authored "Data-Driven Smart Manufacturing Technologies for Prop Shop Systems."