Modeling of Failure Prediction Bayesian Network with Divide-and-Conquer Principle

For system failure prediction, automatically modeling from historical failure dataset is one of the challenges in practical engineering fields. In this paper, an effective algorithm is proposed to build the failure prediction Bayesian network (FPBN) model with data mining technology. First, the conc...

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Published inMathematical problems in engineering Vol. 2014; no. 2014; pp. 1 - 8
Main Authors Sun, Shu-dong, Si, Shubin, Si, Weitao, Cai, Zhi-qiang
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Puplishing Corporation 01.01.2014
Hindawi Publishing Corporation
Hindawi Limited
Subjects
Algorithms
Bayesian analysis
Compounds
Computer simulation
Conditional probability
Construction
Data mining
Decision theory
Effectiveness
Efficiency
Engineering
Failure
Helicopters
Information management
Mathematical models
Maximum likelihood estimation
Modelling
Network management systems
Networks
Nodes
Software reliability
Statistical methods
Studies
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Summary:For system failure prediction, automatically modeling from historical failure dataset is one of the challenges in practical engineering fields. In this paper, an effective algorithm is proposed to build the failure prediction Bayesian network (FPBN) model with data mining technology. First, the conception of FPBN is introduced to describe the state of components and system and the cause-effect relationships among them. The types of network nodes, the directions of network edges, and the conditional probability distributions (CPDs) of nodes in FPBN are discussed in detail. According to the characteristics of nodes and edges in FPBN, a divide-and-conquer principle based algorithm (FPBN-DC) is introduced to build the best FPBN network structures of different types of nodes separately. Then, the CPDs of nodes in FPBN are calculated by the maximum likelihood estimation method based on the built network. Finally, a simulation study of a helicopter convertor model is carried out to demonstrate the application of FPBN-DC. According to the simulations results, the FPBN-DC algorithm can get better fitness value with the lower number of iterations, which verified its effectiveness and efficiency compared with traditional algorithm.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2014/210714