TY - GEN
T1 - Production system performance prediction model based on manufacturing big data
AU - Zhang, Yingfeng
AU - Liu, Sichao
AU - Si, Shubin
AU - Yang, Haidong
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - 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.
AB - 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.
KW - dynamic bayesian networks
KW - manufacturing big data
KW - performance prediction
UR - http://www.scopus.com/inward/record.url?scp=84941207527&partnerID=8YFLogxK
U2 - 10.1109/ICNSC.2015.7116048
DO - 10.1109/ICNSC.2015.7116048
M3 - 会议稿件
AN - SCOPUS:84941207527
T3 - ICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control
SP - 277
EP - 280
BT - ICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 12th IEEE International Conference on Networking, Sensing and Control, ICNSC 2015
Y2 - 9 April 2015 through 11 April 2015
ER -