摘要
针对管道样本量少、退化数据不足造成寿命预测不准确的问题，提出了基于维纳过程（WienerProcess）的贝叶斯信息融合法以实现腐蚀油气管道剩余寿命的实时预测。首先通过双应力加速退化试验获得退化数据。并结合现场实测数据建立剩余寿命预测模型；然后利用马尔科夫链蒙特卡洛（MCMC）估计未知参数；最后以某型管道为例，验证所提方法的合理性和正确性。结果表明：利用加速退化试验能大幅度缩短数据获取时间．加速退化数据和少量现场实测数据相结合，运用贝叶斯方法能够提高参数估计的精度，从而提高剩余寿命预测的准确性，该方法可应用于管道的可靠性评价中。
In view of the small sample pipeline and insufficient degradation data that could not meet the requirement of accurate prediction of residual lifetime, this paper used a Bayesian information fusion method based on Wiener process to predict the residual lifetime of the corroded pipelines. Firstly, the degenerated data were obtained by the double- stress constant accelerated degradation test, and the predict model of residual lifetime was established based on measured data. Then the Markov Chain Monte Carlo （MCMC） method was used to estimate the unknown parameters. At last, a certain type of service pipeline as an example was used to verify the rationality and correctness of the proposed method. Results showed that using accelerated degradation tests could greatly shorten the data acquisition time. The accuracy of parameter estimation was improved by Bayesian method with the combination of accelerated degradation data and a small amount of field data, and then the residual lifetime could be predicted accurately. Therefore, the method can be applied to the reliability evaluation of pipelines.
作者
张新生
吕品品
王明虎
张平
ZHANG Xin- sheng; LYU Pin- pin; WANG Ming- hu; ZHANG Ping;(School of Management, Xi＇ an University of Architecture and Technology, Xi＇ an 710055, China)
出处
《材料保护》
CAS
CSCD
北大核心
2018年第10期59-65,共7页
Materials Protection
基金
国家自然科学基金(41877527)
陕西省自然科学基金(2016JM6023)
陕西省社科基金项目(2018S34)资助
关键词
腐蚀管道
剩余寿命预测
加速退化试验
贝叶斯信息融合
马尔科夫链蒙特卡洛
corroded pipelines
residual lifetime prediction
accelerated degradation test
Bayesian information fusion
Markov Chain Monte Carlo
