@inproceedings{877318407b934ca0acebcf06a86e3be7,
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",
doi = "10.1109/SERA57763.2023.10197769",
language = "English",
series = "Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023",
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",
}
