REAL-TIME RELIABILITY PREDICTION METHOD BASED ON MARKOV CHAIN MONTE CARLO
REAL-TIME RELIABILITY PREDICTION METHOD BASED ON MARKOV CHAIN MONTE CARLO
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发表时间：
2007
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通讯作者：
Xuzhen Zheng
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作者：
Xuzhen Zheng
针对产品非线性退化路径模型中误差项方差未知时的实时可靠性预测问题，提出了一种贝叶斯分析方法，将误差项标准差的无信息先验引入贝叶斯分析中，给出了退化路径模型中随机系数的先验密度，并将其应用于实时可靠性预测中。采用马尔可夫链蒙特卡罗方法生成随机系数和未知标准差的联合后验分布样本，通过对疲劳裂纹扩展数据的计算，验证了该方法的有效性。
In order to predict the product's real-time reliability when the variance of error terms in its nonlinear degradation path model was unknown,a Bayesian analysis method was presented.The noninformative prior of the standard deviation of error terms was introduced for Bayesian analysis.Then,given the prior density of the random coefficients in the degradation path model,the Markov Chain Monte Carlo method was adopted to generate samples of joint posterior distribution of random coefficients and unknown standard deviation.By these posterior samples,the product's reliability during a certain period in the future was predicted.The numerical example by use of fatigue crack growth data shows the effectiveness of the proposed method.