Identifying product failure rate based on a conditional Bayesian network classifier

• Published in: Expert systems with applications Vol. 38; no. 5; pp. 5036 - 5043
• Main Authors: Cai, Zhiqiang, Sun, Shudong, Si, Shubin, Yannou, Bernard
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2011; Elsevier
• Subjects: Bayesian analysis; Bayesian network; Classification; Classifier; Classifiers; Computer Science; Conditional independence; Decision trees; Engineering Sciences; Failure rate; Failure rates; Maintenance management; Mathematical models; Mechanics; Modeling and Simulation; Networks; Quality; Conditional independence; Classifier; Failure rate; Bayesian network; Maintenance management
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2010.09.146

► CBN introduces the conditional independence relationships among attribute variables. ► CBN provides an effective approach to classify the failure rate rank of products. ► CBN increases the classification accuracy. ► CBN makes an acceptable balance between classifier complexity and performance. 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.