A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems

Caoimhe M. Carbery; Roger Woods; Adele H. Marshall

doi:10.1007/978-981-13-2384-3_16

A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems

Caoimhe M. Carbery^*, Roger Woods, Adele H. Marshall

^*Corresponding author for this work

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

4 Citations (Scopus)

376 Downloads (Pure)

Abstract

Recent emphasis has been placed on improving the processes in manufacturing by employing early detection or fault prediction within production lines. Whilst companies are increasingly including sensors to record observations and measurements, this brings challenges in interpretation as standard approaches for artificial intelligence (AI) do not highlight the presence of unknown relationships. To address this, we propose a new data analytics framework for predicting faults in a large-scale manufacturing system and validate it using a publicly available Bosch manufacturing dataset with a focus on pre-processing of the data.

Original language	English
Title of host publication	Intelligent Computing and Internet of Things - First International Conference on Intelligent Manufacturing and Internet of Things and 5th International Conference on Computing for Sustainable Energy and Environment, IMIOT and ICSEE 2018, Proceedings
Editors	Zhile Yang, Dongsheng Yang, Kang Li, Minrui Fei, Dajun Du
Publisher	Springer-Verlag
Pages	169-179
Number of pages	11
ISBN (Print)	9789811323836
DOIs	https://doi.org/10.1007/978-981-13-2384-3_16
Publication status	Published - 04 Sep 2018
Event	1st International Conference on Intelligent Manufacturing and Internet of Things, IMIOT 2018 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2018 - Chogqing, China Duration: 21 Sep 2018 → 23 Sep 2018

Publication series

Name	Communications in Computer and Information Science
Volume	924
ISSN (Print)	1865-0929

Conference

Conference	1st International Conference on Intelligent Manufacturing and Internet of Things, IMIOT 2018 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2018
Country/Territory	China
City	Chogqing
Period	21/09/2018 → 23/09/2018

ASJC Scopus subject areas

Computer Science(all)
Mathematics(all)

Access to Document

10.1007/978-981-13-2384-3_16Licence: Unspecified

A new data analytics framework emphasising pre-processing in learning AI models for complex manufacturing systems
© 2018 Springer Nature Singapore Pte Ltd. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 593 KBLicence: Unspecified

Cite this

Carbery, C. M., Woods, R., & Marshall, A. H. (2018). A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems. In Z. Yang, D. Yang, K. Li, M. Fei, & D. Du (Eds.), Intelligent Computing and Internet of Things - First International Conference on Intelligent Manufacturing and Internet of Things and 5th International Conference on Computing for Sustainable Energy and Environment, IMIOT and ICSEE 2018, Proceedings (pp. 169-179). (Communications in Computer and Information Science; Vol. 924). Springer-Verlag. https://doi.org/10.1007/978-981-13-2384-3_16

Carbery, Caoimhe M. ; Woods, Roger ; Marshall, Adele H. / A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems. Intelligent Computing and Internet of Things - First International Conference on Intelligent Manufacturing and Internet of Things and 5th International Conference on Computing for Sustainable Energy and Environment, IMIOT and ICSEE 2018, Proceedings. editor / Zhile Yang ; Dongsheng Yang ; Kang Li ; Minrui Fei ; Dajun Du. Springer-Verlag, 2018. pp. 169-179 (Communications in Computer and Information Science).

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abstract = "Recent emphasis has been placed on improving the processes in manufacturing by employing early detection or fault prediction within production lines. Whilst companies are increasingly including sensors to record observations and measurements, this brings challenges in interpretation as standard approaches for artificial intelligence (AI) do not highlight the presence of unknown relationships. To address this, we propose a new data analytics framework for predicting faults in a large-scale manufacturing system and validate it using a publicly available Bosch manufacturing dataset with a focus on pre-processing of the data.",

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booktitle = "Intelligent Computing and Internet of Things - First International Conference on Intelligent Manufacturing and Internet of Things and 5th International Conference on Computing for Sustainable Energy and Environment, IMIOT and ICSEE 2018, Proceedings",

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Carbery, CM, Woods, R & Marshall, AH 2018, A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems. in Z Yang, D Yang, K Li, M Fei & D Du (eds), Intelligent Computing and Internet of Things - First International Conference on Intelligent Manufacturing and Internet of Things and 5th International Conference on Computing for Sustainable Energy and Environment, IMIOT and ICSEE 2018, Proceedings. Communications in Computer and Information Science, vol. 924, Springer-Verlag, pp. 169-179, 1st International Conference on Intelligent Manufacturing and Internet of Things, IMIOT 2018 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2018, Chogqing, China, 21/09/2018. https://doi.org/10.1007/978-981-13-2384-3_16

A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems. / Carbery, Caoimhe M.; Woods, Roger ; Marshall, Adele H.

Intelligent Computing and Internet of Things - First International Conference on Intelligent Manufacturing and Internet of Things and 5th International Conference on Computing for Sustainable Energy and Environment, IMIOT and ICSEE 2018, Proceedings. ed. / Zhile Yang; Dongsheng Yang; Kang Li; Minrui Fei; Dajun Du. Springer-Verlag, 2018. p. 169-179 (Communications in Computer and Information Science; Vol. 924).

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

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AU - Marshall, Adele H.

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AB - Recent emphasis has been placed on improving the processes in manufacturing by employing early detection or fault prediction within production lines. Whilst companies are increasingly including sensors to record observations and measurements, this brings challenges in interpretation as standard approaches for artificial intelligence (AI) do not highlight the presence of unknown relationships. To address this, we propose a new data analytics framework for predicting faults in a large-scale manufacturing system and validate it using a publicly available Bosch manufacturing dataset with a focus on pre-processing of the data.

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Carbery CM, Woods R , Marshall AH. A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems. In Yang Z, Yang D, Li K, Fei M, Du D, editors, Intelligent Computing and Internet of Things - First International Conference on Intelligent Manufacturing and Internet of Things and 5th International Conference on Computing for Sustainable Energy and Environment, IMIOT and ICSEE 2018, Proceedings. Springer-Verlag. 2018. p. 169-179. (Communications in Computer and Information Science). doi: 10.1007/978-981-13-2384-3_16

A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Statistical data analysis of industrial systems

Cite this

A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Student theses

Statistical data analysis of industrial systems

Cite this