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Reliability Prediction System using Bayesian Network

机译：贝叶斯网络的可靠性预测系统

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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.
机译：本文介绍了一种使用制造参数数据的产品现场故障预测方法，其中应用了贝叶斯网络算法。贝叶斯网络模型估计需要解决两个技术问题。首先，制造过程数据在许多情况下可能不是正态分布。第二，失败率可能小于1％，因此失败数据的数量比通过数据少得多。为了解决这些问题，评估了分箱方法和参数选择方法。我们已经开发出一种分箱方法，可以将每个分箱的数据划分为相等的数据大小。该算法将减少通过数据对网络估计的影响。参数选择方法基于从传递数据分布中观察故障数据的概率。该算法与称为K2算法的贝叶斯网络估计算法匹配。我们的方法与另一种分箱方法进行了比较，该方法将每个分箱的数据划分为相等的间隔，并基于U检验选择参数。总之，通过使用实际数据的实验，我们的方法显示出比其他方法更高的预测准确性。

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