Gaussian Process Models for Manufacturing and Industrial Processes

Jamie Wilson; Jack Moore; Norma Bargary; Kevin Burke

doi:10.1109/SWC57546.2023.10449006

Gaussian Process Models for Manufacturing and Industrial Processes

Jamie Wilson, Jack Moore, Norma Bargary, Kevin Burke

Department of Mathematics and Statistics

University of Limerick

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

Abstract

Gaussian processes are a class of probabilistic models which offer a flexible approach to the statistical modelling of arbitrary functions. Their robust uncertainty estimates and ability to handle complex and non-linear relationships makes them particularly effective for time-series interpolation and prediction problems. Here, we give a brief overview of the mathematical and conceptual framework for Gaussian processes and demonstrate their versatility for time-series analysis through illustrative examples of their application to frequently encountered scenarios involving manufacturing and industrial data. Such examples serve to highlight the flexibility and effectiveness of Gaussian processes for modelling diverse phenomena, monitoring and predicting machine performance and enabling probabilistic predictions and data-driven decision-making in the presence of uncertainty.

Original language	English
Title of host publication	Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350319804
DOIs	https://doi.org/10.1109/SWC57546.2023.10449006
Publication status	Published - 2023
Event	9th IEEE Smart World Congress, SWC 2023 - Portsmouth, United Kingdom Duration: 28 Aug 2023 → 31 Aug 2023

Publication series

Name	Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023

Conference

Conference	9th IEEE Smart World Congress, SWC 2023
Country/Territory	United Kingdom
City	Portsmouth
Period	28/08/23 → 31/08/23

Keywords

forecasting
Gaussian processes
industry data
process mining
time series
uncertainty quantification

Access to Document

10.1109/SWC57546.2023.10449006

Cite this

Wilson, J., Moore, J., Bargary, N., & Burke, K. (2023). Gaussian Process Models for Manufacturing and Industrial Processes. In Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023 (Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SWC57546.2023.10449006

Wilson, Jamie ; Moore, Jack ; Bargary, Norma et al. / Gaussian Process Models for Manufacturing and Industrial Processes. Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023).

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title = "Gaussian Process Models for Manufacturing and Industrial Processes",

abstract = "Gaussian processes are a class of probabilistic models which offer a flexible approach to the statistical modelling of arbitrary functions. Their robust uncertainty estimates and ability to handle complex and non-linear relationships makes them particularly effective for time-series interpolation and prediction problems. Here, we give a brief overview of the mathematical and conceptual framework for Gaussian processes and demonstrate their versatility for time-series analysis through illustrative examples of their application to frequently encountered scenarios involving manufacturing and industrial data. Such examples serve to highlight the flexibility and effectiveness of Gaussian processes for modelling diverse phenomena, monitoring and predicting machine performance and enabling probabilistic predictions and data-driven decision-making in the presence of uncertainty.",

keywords = "forecasting, Gaussian processes, industry data, process mining, time series, uncertainty quantification",

author = "Jamie Wilson and Jack Moore and Norma Bargary and Kevin Burke",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th IEEE Smart World Congress, SWC 2023 ; Conference date: 28-08-2023 Through 31-08-2023",

year = "2023",

doi = "10.1109/SWC57546.2023.10449006",

language = "English",

series = "Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023",

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

booktitle = "Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023",

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Wilson, J, Moore, J, Bargary, N & Burke, K 2023, Gaussian Process Models for Manufacturing and Industrial Processes. in Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023. Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023, Institute of Electrical and Electronics Engineers Inc., 9th IEEE Smart World Congress, SWC 2023, Portsmouth, United Kingdom, 28/08/23. https://doi.org/10.1109/SWC57546.2023.10449006

Gaussian Process Models for Manufacturing and Industrial Processes. / Wilson, Jamie; Moore, Jack; Bargary, Norma et al.
Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023).

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

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AB - Gaussian processes are a class of probabilistic models which offer a flexible approach to the statistical modelling of arbitrary functions. Their robust uncertainty estimates and ability to handle complex and non-linear relationships makes them particularly effective for time-series interpolation and prediction problems. Here, we give a brief overview of the mathematical and conceptual framework for Gaussian processes and demonstrate their versatility for time-series analysis through illustrative examples of their application to frequently encountered scenarios involving manufacturing and industrial data. Such examples serve to highlight the flexibility and effectiveness of Gaussian processes for modelling diverse phenomena, monitoring and predicting machine performance and enabling probabilistic predictions and data-driven decision-making in the presence of uncertainty.

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Wilson J, Moore J, Bargary N , Burke K. Gaussian Process Models for Manufacturing and Industrial Processes. In Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023). doi: 10.1109/SWC57546.2023.10449006

Gaussian Process Models for Manufacturing and Industrial Processes

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Keywords

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