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 |
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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 | |
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 |
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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 |
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Country/Territory | China |
City | Chogqing |
Period | 21/09/2018 → 23/09/2018 |
ASJC Scopus subject areas
- Computer Science(all)
- Mathematics(all)
Fingerprint
Dive into the research topics of 'A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems'. Together they form a unique fingerprint.Student theses
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Statistical data analysis of industrial systems
Author: Carbery, C., Dec 2019Supervisor: Woods, R. (Supervisor) & Marshall, A. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy
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