Reliability Prediction System using Bayesian Network  
Author Kei Imazawa


Co-Author(s) Y. Katsumura; K. Kosugi; M. Douwaki


Abstract This paper describes a product field failure prediction method using manufacturing parametric data where the Bayesian Network algorithm is applied. There are two technical subjects to be solved in the Bayesian Network model estimation. First, manufacturing process data may not be a normal distribution in many cases. Second, the failure ratio could be less than 1% so that the amount of failure data is much smaller than pass data. To solve these subjects, binning method and parameter selection method are evaluated. We have developed a binning method divides the data to equalized data size for each bin. This algorithm will reduce an impact of pass data in the network estimation. The parameter selection method is based on probability of observing the failure data from pass data distribution. This algorithm matches with Bayesian Network estimation algorithm called K2 algorithm. Our method is compared with another binning method which divides the data to equal interval for each bin and parameter selection method based on U test. In conclusion, our method shows higher prediction accuracy than the another method, by our experiments using actual data.


Keywords Reliability Prediction, Bayesian Network, Probability of Occurrence
    Article #:  1812
Proceedings of the 18th ISSAT International Conference on Reliability and Quality in Design
July 26-28, 2012 - Boston, Massachusetts, U.S.A.