基于退化数据的高可靠性产品贝叶斯分类决策

摘要/Abstract

摘要：

摘要：针对高可靠性产品的退化数据，提出一种贝叶斯分类方法，将产品按最大后验概率进行分类.利用非线性Wiener过程模型来描述产品的退化路径，提出了一种结合期望最大化（EM）与K均值聚类的算法以用于估计模型的未知参数，建立了平均成本最小化的最优分类决策模型.实例与仿真试验显示，该分类方法具有较高的分类精度与较小的成本.

关键词: , 退化数据, 贝叶斯分类, 期望最大化, K均值聚类算法, 平均成本

Abstract:

Abstract： A Bayesian classification method based on degradation data of highly reliable products was proposed. The products were classified according to the maximum posterior probability. Then, the nonlinear Wiener process model was used to describe the degradation paths of the products. Next, an algorithm which combined the expectation maximization (EM) method with Kmeans clustering was developed to estimate the unknown parameters of the model. After that, an average cost model was established to make the optimal classification policies. Finally, an example and a simulation were presented to illustrate the effectiveness of the proposed method.

Key words: degradation data, Bayesian classification, expectation maximization (EM), Kmeans, average cost model

中图分类号:

TP 202.1

陈震，潘尔顺. 基于退化数据的高可靠性产品贝叶斯分类决策[J]. 上海交通大学学报（自然版）, 2017, 51(1): 76-.

CHEN Zhen,PAN Ershun. A Bayesian Classification Policy for Highly Reliable Products Based on Degradation Data[J]. Journal of Shanghai Jiaotong University, 2017, 51(1): 76-.

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