TY - JOUR
T1 - IoT-Enabled Real-Time Production Performance Analysis and Exception Diagnosis Model
AU - Zhang, Yingfeng
AU - Wang, Wenbo
AU - Wu, Naiqi
AU - Qian, Cheng
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/7
Y1 - 2016/7
N2 - The recent developments of technologies in Internet of Things (IoT) provide the opportunities for smart manufacturing with real-time traceability, visibility, and interoperability in production planning, execution, and control. To fulfill this target, this work presents a real-time production performance analysis and exception diagnosis model (PAEDM). By this model, hierarchical-timed-colored Petri net (HTCPN) with smart tokens that change just like smart objects in practice is used to analyze the sensor data such that the critical performance information can be perceived. Decision Tree is used to diagnose exceptions from the critical production performance, so that persuasive qualitative and quantitative exception information can be extracted accurately. The presented method is demonstrated by a case study and simulation results show that PAEDM can be used to effectively analyze production performance and exceptions in real-time for dynamic and stochastic manufacturing processes.
AB - The recent developments of technologies in Internet of Things (IoT) provide the opportunities for smart manufacturing with real-time traceability, visibility, and interoperability in production planning, execution, and control. To fulfill this target, this work presents a real-time production performance analysis and exception diagnosis model (PAEDM). By this model, hierarchical-timed-colored Petri net (HTCPN) with smart tokens that change just like smart objects in practice is used to analyze the sensor data such that the critical performance information can be perceived. Decision Tree is used to diagnose exceptions from the critical production performance, so that persuasive qualitative and quantitative exception information can be extracted accurately. The presented method is demonstrated by a case study and simulation results show that PAEDM can be used to effectively analyze production performance and exceptions in real-time for dynamic and stochastic manufacturing processes.
KW - Decision tree
KW - internet of things(IoT)
KW - manufacturing system
KW - performance analysis and exception diagnosis
KW - Petri net
UR - http://www.scopus.com/inward/record.url?scp=84949818116&partnerID=8YFLogxK
U2 - 10.1109/TASE.2015.2497800
DO - 10.1109/TASE.2015.2497800
M3 - 文章
AN - SCOPUS:84949818116
SN - 1545-5955
VL - 13
SP - 1318
EP - 1332
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 3
M1 - 7336570
ER -