.png){kind=icon}
返回
Bayesian inference method for stochastic damage accumulation modeling
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ress.2012.11.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Damage accumulation based reliability model plays an increasingly important role in successful realization of condition based maintenance for complicated engineering systems. This paper developed a Bayesian framework to establish stochastic damage accumulation model from historical inspection data, considering data uncertainty. Proportional hazards modeling technique is developed to model the nonlinear effect of multiple influencing factors on system reliability. Different from other hazard modeling techniques such as normal linear regression model, the approach does not require any distribution assumption for the hazard model, and can be applied for a wide variety of distribution models. A Bayesian network is created to represent the nonlinear proportional hazards models and to estimate model parameters by Bayesian inference with Markov Chain Monte Carlo simulation. Both qualitative and quantitative approaches are developed to assess the validity of the established damage accumulation model. Anderson-Darling goodness-of-fit test is employed to perform the normality test, and Box-Cox transformation approach is utilized to convert the non-normality data into normal distribution for hypothesis testing in quantitative model validation. The methodology is illustrated with the seepage data collected from real-world subway tunnels. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Bayesian inference
Box-Cox transformation
CBM
Damage accumulation model
Model validation
Proportional hazard model
期刊
R
IF:
11
论文数:
9.0K
被引数:
4.2W
机构
引用论文
.png){kind=icon}
A review on machinery diagnostics and prognostics implementing condition-based maintenance实施状态维修的机械诊断和预测综述
PREAI
分享收藏A probability model for investigating the trend of structural deterioration of wastewater pipelines
PREAI
分享收藏分享收藏
查看更多内容