Abstract Failure modelling and reliability assessment of repairable systems has been receiving a great deal of attention due to its pivotal role in risk and safety management of process industries. Meanwhile, the level of uncertainty that comes with characterizing the parameters of reliability models require a sound parameter estimator tool. For the purpose of comparison and cross-verification, this paper aims at identifying the most efficient and minimal variance parameter estimator. Hierarchical Bayesian modelling (HBM) and Maximum Likelihood Estimation (MLE) approaches are applied to investigate the effect of utilizing observed data on inter-arrival failure time modelling. A case study of Natural Gas Regulating and Metering Stations in Italy has been considered to illustrate the application of proposed framework. The results highlight that relaxing the renewal process assumption and taking the time dependency of the observed data into account will result in more precise failure models. The outcomes of this study can help asset managers to find the optimum approach to reliability assessment of repairable systems.

中文翻译：

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使用分层贝叶斯推理和最大似然估计的可修复系统的可靠性挑战

摘要 可修复系统的失效建模和可靠性评估因其在过程工业风险和安全管理中的关键作用而受到广泛关注。同时，表征可靠性模型参数所带来的不确定性水平需要一个可靠的参数估计器工具。出于比较和交叉验证的目的，本文旨在确定最有效和最小方差的参数估计器。应用分层贝叶斯建模 (HBM) 和最大似然估计 (MLE) 方法来研究利用观测数据对到达间故障时间建模的影响。已考虑意大利天然气调节和计量站的案例研究来说明拟议框架的应用。结果强调，放宽更新过程假设并考虑观察数据的时间依赖性将导致更精确的故障模型。这项研究的结果可以帮助资产管理者找到对可修复系统进行可靠性评估的最佳方法。