A Bayesian network based learning system for modelling faults in large-scale manufacturing

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
371 Downloads (Pure)

Abstract

Manufacturing companies can benefit from the early prediction and detection of failures to improve their product yield and reduce system faults through advanced data analytics. Whilst an abundance of data on their processing systems exist, they face difficulties in using it to gain insights to improve their systems. Bayesian networks (BNs) are considered here for diagnosing and predicting faults in a large manufacturing dataset from Bosch. Whilst BN structure learning has been performed traditionally on smaller sized data, this work demonstrates the ability to learn an appropriate BN structure for a large dataset with little information on the variables, for the first time. This paper also demonstrates a new framework for creating an appropriate probabilistic model for the Bosch dataset through the selection of statistically important variables on the response; this is then used to create a BN network which can be used to answer probabilistic queries and classify products based on changes in the sensor values in the production process.
Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1357-1362
Number of pages6
Volume2018-February
ISBN (Electronic)9781509059492
ISBN (Print)978-1-5386-4053-1
DOIs
Publication statusPublished - 30 Apr 2018
Event19th IEEE International Conference on Industrial Technology, ICIT 2018 - Lyon, France
Duration: 19 Feb 201822 Feb 2018

Conference

Conference19th IEEE International Conference on Industrial Technology, ICIT 2018
CountryFrance
CityLyon
Period19/02/201822/02/2018

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Carbery, C. M., Woods, R., & Marshall, A. H. (2018). A Bayesian network based learning system for modelling faults in large-scale manufacturing. In Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018 (Vol. 2018-February, pp. 1357-1362). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT.2018.8352377