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Production System Performance Prediction Model Based on Manufacturing Big Data
Department of Industry Engineering, Northwestern Polytechnical University, Xi'an, China.
2015 (English)In: ICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control, IEEE, 2015, p. 277-280Conference paper, Published paper (Refereed)
Abstract [en]

Existing production systems are short of real-time performance status of production process active perception, resulting in the production abnormal conditions processed lag, leading to the frequency problems of deviations in production tasks execution and planning. To address this problem, in this research, an advanced identification technology is extended to the manufacturing field to acquire the real-time performance data. Based on the sensed real-time manufacturing data, this paper presents a prediction method of production system performance by applying the Dynamic Bayesian Networks (DBN) theory and methods. It aims to achieve the prediction of the performance status of production system and potential anomalies, and to provide the important and abundant prediction information for real-time production control.

Place, publisher, year, edition, pages
IEEE, 2015. p. 277-280
Series
IEEE International Conference on Networking Sensing and Control, ISSN 1810-7869
Keywords [en]
performance prediction; dynamic bayesian networks; manufacturing big data
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-253560DOI: 10.1109/ICNSC.2015.7116048ISI: 000380543900049Scopus ID: 2-s2.0-84941207527ISBN: 978-1-4799-8069-7 (print)OAI: oai:DiVA.org:kth-253560DiVA, id: diva2:1325444
Conference
2015 12th IEEE International Conference on Networking, Sensing and Control, ICNSC 2015; Howard International HouseTaipei; Taiwan; 9 April 2015 through 11 April 2015
Note

QC 20190626

Available from: 2019-06-15 Created: 2019-06-15 Last updated: 2019-06-26Bibliographically approved

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Liu, Sichao

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