TY - GEN
T1 - Integration of failure prediction Bayesian networks for complex equipment system
AU - Si, Weitao
AU - Cai, Zhiqiang
AU - Sun, Shudong
AU - Si, Shubin
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - With the advantages of the modularization concept, this paper proposes an integration modeling method of failure prediction Bayesian network (FPBN) for failure prediction of complex equipment system. First of all, the definition of failure prediction Bayesian network module (FPBNM) is introduced and described. Then, when the complex equipment system is decomposed into some subsystems and represented with a set of related FPBN models, the corresponding modularization method of FPBN to FPBNM and the integration method of FPBNM models are discussed in details. Moreover, based on the super node mode of integrated FPBN model, this paper proposes a convenient and efficient inference algorithm. Finally, the case study of FPBN integration for an airplane head up display (HUD) system is carried out. The result shows that the proposed integration method of FPBN could build and inference the practical model efficiently for such complex equipment system.
AB - With the advantages of the modularization concept, this paper proposes an integration modeling method of failure prediction Bayesian network (FPBN) for failure prediction of complex equipment system. First of all, the definition of failure prediction Bayesian network module (FPBNM) is introduced and described. Then, when the complex equipment system is decomposed into some subsystems and represented with a set of related FPBN models, the corresponding modularization method of FPBN to FPBNM and the integration method of FPBNM models are discussed in details. Moreover, based on the super node mode of integrated FPBN model, this paper proposes a convenient and efficient inference algorithm. Finally, the case study of FPBN integration for an airplane head up display (HUD) system is carried out. The result shows that the proposed integration method of FPBN could build and inference the practical model efficiently for such complex equipment system.
KW - Complex equipment system
KW - failure prediction Bayesian network
KW - integration
KW - modularization
UR - https://www.scopus.com/pages/publications/84988290579
U2 - 10.1109/IEEM.2014.7058821
DO - 10.1109/IEEM.2014.7058821
M3 - 会议稿件
AN - SCOPUS:84988290579
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 1161
EP - 1165
BT - IEEM 2014 - 2014 IEEE International Conference on Industrial Engineering and Engineering Management
PB - IEEE Computer Society
T2 - 2014 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2014
Y2 - 9 December 2014 through 12 December 2014
ER -