考虑随机效应的腐蚀管道贝叶斯退化分析

doi:10.16265/j.cnki.issn1003-3033.2019.08.012

中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (8): 73-80.doi: 10.16265/j.cnki.issn1003-3033.2019.08.012

考虑随机效应的腐蚀管道贝叶斯退化分析

张新生教授, 吕品品

西安建筑科技大学管理学院,陕西西安 710055

收稿日期:2019-04-25 修回日期:2019-07-04 发布日期:2020-10-21
作者简介:张新生 (1978—),男,河南驻马店人,博士,教授,主要从事管道风险评估理论、建模与方法、智能信息处理等方面的教学和科研工作。E-mail: xinsheng.zh@outlook.com。
基金资助:
国家自然科学基金资助(41877527);陕西省社科基金资助(2018S34)。

Bayesian degradation analysis of corroded pipeline considering random effect

ZHANG Xinsheng, LYU Pinpin

School of Management, Xi'an University of Architecture & Technology, Xi'an Shaanxi 710055,China

Received:2019-04-25 Revised:2019-07-04 Published:2020-10-21

摘要/Abstract

摘要： 为提高管道腐蚀退化分析模型的适用性和预测精度,加强管道安全性管理,提出一种通用的基于贝叶斯理论考虑随机效应的逆高斯(IG)过程退化分析方法。根据系统退化随机效应信息,并结合直接处理在线预测数据集的方法,利用贝叶斯方法建立一个简单的IG模型和随机漂移(RD)、随机波动(RV)、随机漂移-波动(RDV)等3个随机效应IG模型。基于相关模型参数产生的后验样本,通过贝叶斯χ²优度检验得到的概率分别为简单IG模型为0.981 3;RD-IG模型为1.00;RV-IG模型为0.925;RDV-IG模型为0.994 7。案例结果表明:RD-IG模型对数据拟合良好,贝叶斯分析方法在实时监测场景下进行退化预测灵活性强;考虑随机效应的退化分析能提高预测的准确性。

关键词: 腐蚀管道, 逆高斯(IG), 随机效应, 贝叶斯理论, 马尔科夫链蒙特卡罗(MCMC)

Abstract: To improve the applicability and prediction accuracy of pipeline corrosion degradation analysis model and strengthen pipeline safety management, a general Bayesian-based IG process degradation analysis method considering random effect was proposed. According to the random effect information of system degradation and the method of directly processing online prediction dataset, a simple IG model and three IG models with random effects were established by Bayesian method. Based on posterior samples generated by relevant model parameters, the probabilities obtained by Bayesian goodness test were 0.9813 for simple IG process model, 1.00 for Random Drift(RD)-IG model, 0.925 for Random Volatility(RV)-IG model, and 0.9947 for Random Drift-Volatility(RDV)-IG model. The results show that the RD-IG model fits well with data, that the Bayesian analysis method is flexible and has better effect on degradation prediction in the real-time monitoring scenario, and that the degradation analysis considering random effect can improve the accuracy of prediction.

Key words: corroded pipeline, inverse Gaussian (IG), random effect, Bayesian method, degradation model, Markov chain Monte Carlo(MCMC)

中图分类号:

X937

张新生, 吕品品. 考虑随机效应的腐蚀管道贝叶斯退化分析[J]. 中国安全科学学报, 2019, 29(8): 73-80.

ZHANG Xinsheng, LYU Pinpin. Bayesian degradation analysis of corroded pipeline considering random effect[J]. China Safety Science Journal, 2019, 29(8): 73-80.

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考虑随机效应的腐蚀管道贝叶斯退化分析

Bayesian degradation analysis of corroded pipeline considering random effect

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