Topological Data Analysis in smart manufacturing processes -- A survey on the state of the art DOI Creative Commons
Martin Uray, Barbara Giunti,

Michael Kerber

и другие.

arXiv (Cornell University), Год журнала: 2023, Номер unknown

Опубликована: Янв. 1, 2023

Topological Data Analysis (TDA) is a discipline that applies algebraic topology techniques to analyze complex, multi-dimensional data. Although it relatively new field, TDA has been widely and successfully applied across various domains, such as medicine, materials science, biology. This survey provides an overview of the state art within dynamic promising application area: industrial manufacturing production, particularly Industry 4.0 context. We have conducted rigorous reproducible literature search focusing on applications in production settings. The identified works are categorized based their areas process types input highlight principal advantages tools this context, address challenges encountered future potential field. Furthermore, we identify methods currently underexploited specific discuss how could be beneficial, with aim stimulating further research work seeks bridge theoretical advancements practical needs production. Our goal serve guide for practitioners researchers applying systems. advocate untapped domain encourage continued exploration research.

Язык: Английский

Supervised Functional State-Space Modeling for Monitoring Multigrade Batch Processes with Irregular Data Using Meta-learning DOI

Lin-Xuan You,

Jingxiang Liu, Junghui Chen

и другие.

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 107122 - 107122

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Topological Data Analysis in smart manufacturing: State of the art and future directions DOI Creative Commons
Martin Uray, Barbara Giunti,

Michael Kerber

и другие.

Journal of Manufacturing Systems, Год журнала: 2024, Номер 76, С. 75 - 91

Опубликована: Июль 27, 2024

Topological Data Analysis (TDA) is a discipline that applies algebraic topology techniques to analyze complex, multi-dimensional data. Although it relatively new field, TDA has been widely and successfully applied across various domains, such as medicine, materials science, biology. This survey provides an overview of the state art within dynamic promising application area: industrial manufacturing production, particularly Industry 4.0 context. We have conducted rigorous reproducible literature search focusing on applications in production settings. The identified works are categorized based their areas process types input highlight principal advantages tools this context, address challenges encountered future potential field. Furthermore, we identify methods currently underexploited specific discuss how could be beneficial, with aim stimulating further research work seeks bridge theoretical advancements practical needs production. Our goal serve guide for practitioners researchers applying systems. advocate untapped domain encourage continued exploration research.

Язык: Английский

Процитировано

0
Quality prediction of multi-stage batch process based on integrated ConvBiGRU with attention mechanism DOI
Kai Liu, Xiaoqiang Zhao, Miao Mou

и другие.

Applied Intelligence, Год журнала: 2024, Номер 55(2)

Опубликована: Дек. 10, 2024

Язык: Английский

Процитировано

0
Understanding Fouling in an Industrial Biorefinery Membrane Separation Process by Feature-Oriented Data-Driven Modeling DOI
Elia Arnese-Feffin, Pierantonio Facco, Daniele Turati

и другие.

Industrial & Engineering Chemistry Research, Год журнала: 2024, Номер 63(20), С. 9136 - 9150

Опубликована: Май 8, 2024

Membrane separation processes are precious assets for biorefineries to separate biomass from the solution containing product after bioconversion in an effective and energy-efficient way. However, fouling can significantly reduce benefits of membrane separations. Effects be reversible, manifesting as short-term process disruption, or irreversible, causing long-term degradation; two actions typically affect one another. Understanding potential causes is paramount importance mitigate this undesired phenomenon improve operation. In study, we perform a comprehensive investigation ultrafiltration operation world's first industrial-scale biorefinery manufacturing 1,4-biobutanediol via renewable raw materials. We use principal component analysis extract information sensor data spanning six months plant Furthermore, resort feature-oriented data-driven modeling address variability batch duration, exploit knowledge enhance on effects fouling. show how approach provide valuable effectiveness cleaning control policies adopted by operators, offer guidelines maintenance schedule. also engineering judgment model interpretation order identify fouling, uncover strong interaction between reversible irreversible plan experimental investigations clarify some detected assess new ones.

Язык: Английский

Процитировано

0
Topological Data Analysis in smart manufacturing processes -- A survey on the state of the art DOI Creative Commons
Martin Uray, Barbara Giunti,

Michael Kerber

и другие.

arXiv (Cornell University), Год журнала: 2023, Номер unknown

Опубликована: Янв. 1, 2023

Topological Data Analysis (TDA) is a discipline that applies algebraic topology techniques to analyze complex, multi-dimensional data. Although it relatively new field, TDA has been widely and successfully applied across various domains, such as medicine, materials science, biology. This survey provides an overview of the state art within dynamic promising application area: industrial manufacturing production, particularly Industry 4.0 context. We have conducted rigorous reproducible literature search focusing on applications in production settings. The identified works are categorized based their areas process types input highlight principal advantages tools this context, address challenges encountered future potential field. Furthermore, we identify methods currently underexploited specific discuss how could be beneficial, with aim stimulating further research work seeks bridge theoretical advancements practical needs production. Our goal serve guide for practitioners researchers applying systems. advocate untapped domain encourage continued exploration research.

Язык: Английский

Процитировано

0