TY - GEN
T1 - Cognitive computing based manufacturing data processing for internet of things in job-shop floor
AU - Wang, Chuang
AU - Jiang, Pingyu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/2
Y1 - 2015/9/2
N2 - The internet of things (IOT) technology is introduced to the job-shop floor to address the barriers between upper management system and the underlying field automation systems, but also brings new problems, namely, the production management data appears explosive growth. For solving this problem, a data processing methodology based on cognitive computing and cognitive informatics is presented. By simulating the human brain information processing to eye, ears, hands, nose, tongue and other sensory organs, the data of job-shop floor IOT is divided into seven layers from bottom to top, and those layers is classified to passive data acquisition layer and active data acquisition layer. Active data acquisition process consists of three phases. The manufacturing data, manufacturing information and manufacturing knowledge are respectively acquired in three phases, and stored in the corresponding type of database in order to realize the fast reading and updating in different ways. Manufacturing information and manufacturing knowledge based on different granularity are also divided into different levels to meet the job-shop floor IOT management requirements for quick and correct decision-making. This methodology not only effectively reduces the scale of job-shop floor IOT management data, but also gives out different judgments for manufacture problems according to different response time requirements. What's more, five key enabling technologies are described in detail, that is, layered reference model of data, the database function model, data processing stage division, manufacturing information acquisition model and manufacturing knowledge hierarchical model.
AB - The internet of things (IOT) technology is introduced to the job-shop floor to address the barriers between upper management system and the underlying field automation systems, but also brings new problems, namely, the production management data appears explosive growth. For solving this problem, a data processing methodology based on cognitive computing and cognitive informatics is presented. By simulating the human brain information processing to eye, ears, hands, nose, tongue and other sensory organs, the data of job-shop floor IOT is divided into seven layers from bottom to top, and those layers is classified to passive data acquisition layer and active data acquisition layer. Active data acquisition process consists of three phases. The manufacturing data, manufacturing information and manufacturing knowledge are respectively acquired in three phases, and stored in the corresponding type of database in order to realize the fast reading and updating in different ways. Manufacturing information and manufacturing knowledge based on different granularity are also divided into different levels to meet the job-shop floor IOT management requirements for quick and correct decision-making. This methodology not only effectively reduces the scale of job-shop floor IOT management data, but also gives out different judgments for manufacture problems according to different response time requirements. What's more, five key enabling technologies are described in detail, that is, layered reference model of data, the database function model, data processing stage division, manufacturing information acquisition model and manufacturing knowledge hierarchical model.
KW - cognitive computing
KW - data processing
KW - information management
KW - internet of things
KW - job-shop floor
UR - https://www.scopus.com/pages/publications/84955297711
U2 - 10.1109/ICMA.2015.7237883
DO - 10.1109/ICMA.2015.7237883
M3 - 会议稿件
AN - SCOPUS:84955297711
T3 - 2015 IEEE International Conference on Mechatronics and Automation, ICMA 2015
SP - 2521
EP - 2526
BT - 2015 IEEE International Conference on Mechatronics and Automation, ICMA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Mechatronics and Automation, ICMA 2015
Y2 - 2 August 2015 through 5 August 2015
ER -