TY - JOUR
T1 - Data-driven smart production line and its common factors
AU - Zhang, Yongping
AU - Cheng, Ying
AU - Wang, Xi Vincent
AU - Zhong, Ray Y.
AU - Zhang, Yingfeng
AU - Tao, Fei
N1 - Publisher Copyright:
© 2019, Springer-Verlag London Ltd., part of Springer Nature.
PY - 2019/7/19
Y1 - 2019/7/19
N2 - Due to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example.
AB - Due to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example.
KW - Common factors
KW - Data-driven
KW - Energy consumption
KW - Integration
KW - Smart production line (SPL)
UR - http://www.scopus.com/inward/record.url?scp=85064345033&partnerID=8YFLogxK
U2 - 10.1007/s00170-019-03469-9
DO - 10.1007/s00170-019-03469-9
M3 - 文章
AN - SCOPUS:85064345033
SN - 0268-3768
VL - 103
SP - 1211
EP - 1223
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
IS - 1-4
ER -
