Identifying product failure rate based on a conditional Bayesian network classifier

Authors:
Zhiqiang Cai;Shudong Sun;Shubin Si;Bernard Yannou
Affiliations:
Ministry of Education, Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, School of Mechantronics, Northwestern Polytechnical University, Xi'an 710072, China;Ministry of Education, Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, School of Mechantronics, Northwestern Polytechnical University, Xi'an 710072, China;Ministry of Education, Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, School of Mechantronics, Northwestern Polytechnical University, Xi'an 710072, China;Laboratoire Genie Industriel, Ecole Centrale Paris, Chatenay-Malabry 92290, France
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

To identify the product failure rate grade under diverse configuration and operation conditions, a new conditional Bayesian networks (CBN) model is brought forward. By indicating the conditional independence relationship between attribute variables given the target variable, this model could provide an effective approach to classify the grade of failure rate. Furthermore, on the basis of the CBN model, the procedure of building product failure rate grade classifier is elaborated with modeling and application. At last, a case study is carried out and the results show that, with comparison to other Bayesian networks classifiers and traditional decision tree C4.5, the CBN model not only increases the total classification accuracy, but also reduces the complexity of network structure.