Abstract Safety Improvement of engineering processes, especially Oil & Gas operations, has gained a lot of attention during the last decades. This fundamental vision results in risk remediation programs, minimizing the risks of failure, and reducing the associated costs for operation and maintenance. As failures may represent serious threats for both humans and the environment, a comprehensive tool is required to employ maintenance and avoid immoderate dangerous consequences. Traditional risk frameworks mainly include estimation approaches, such as Fault Tree (FT) and Event Tree (ET), producing more simplified models than other tools, such as Bayesian inference. The present work aimed at developing an advanced Risk-Based Maintenance (RBM) methodology for prioritizing maintenance operations, by addressing associated uncertainties through the accident modelling of the process. For this purpose, a Hierarchical Bayesian Approach (HBA) is applied to estimate the failure probabilities of each component while a Failure Mode, Effects and Criticality Analysis is performed to assess the severity. With Markov Chain Monte Carlo simulation from likelihood function and prior distribution, the HBA is capable of incorporating the fluctuations and uncertainties associated with operational data including the variability between the source of data and the correlation of observations. Lastly, to make a meaningful difference between different kinds of risk consequences, whether the risk has a direct or indirect loss, the cost of failures of components is accounted for. To demonstrate the application of the methodology, a Natural Gas Reduction and Measuring Station (NGRMS) is taken into account as a case study. The outcome of the case study proofed that PTG and pump are the most failures sensitive components among other if they being left unattended in the operation with an average number of failure occurrences of 67 and 45; While the THT pipelines and THT tank are less sensitive for being considered for major maintenance request with almost average of 5 times in their lifetime. The proposed method can be exploited by maintenance engineers, asset managers, and policymakers to reduce the downtime periods as well as the risks of on-going operations.

中文翻译：

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基于分层贝叶斯的天然气自主调节操作的预测维护

摘要 在过去的几十年里，工程过程的安全改进，特别是石油和天然气操作，已经引起了很多关注。这一基本愿景产生了风险补救计划，最大限度地降低了故障风险，并降低了相关的运营和维护成本。由于故障可能对人类和环境构成严重威胁，因此需要一种综合工具来进行维护并避免过度的危险后果。传统的风险框架主要包括估计方法，如故障树（FT）和事件树（ET），产生比其他工具更简化的模型，如贝叶斯推理。目前的工作旨在开发一种先进的基于风险的维护 (RBM) 方法来确定维护操作的优先级，通过过程的事故建模解决相关的不确定性。为此，应用分层贝叶斯方法 (HBA) 来估计每个组件的故障概率，同时执行故障模式、影响和严重程度分析以评估严重性。通过似然函数和先验分布的马尔可夫链蒙特卡罗模拟，HBA 能够合并与操作数据相关的波动和不确定性，包括数据源之间的可变性和观察的相关性。最后，为了对不同类型的风险后果做出有意义的区分，无论风险是直接损失还是间接损失，都要考虑组件故障的成本。为了演示该方法的应用，将天然气还原和测量站 (NGRMS) 作为案例研究。案例研究的结果证明，如果 PTG 和泵在运行中无人看管，它们是故障最敏感的部件，平均故障发生次数为 67 次和 45 次；而 THT 管道和 THT 罐在其生命周期中几乎平均 5 次被考虑进行重大维护请求时不太敏感。维护工程师、资产经理和政策制定者可以利用所提出的方法来减少停机时间以及持续运营的风险。案例研究的结果证明，如果 PTG 和泵在运行中无人看管，它们是故障最敏感的部件，平均故障发生次数为 67 次和 45 次；而 THT 管道和 THT 罐在其生命周期中几乎平均 5 次被考虑进行重大维护请求时不太敏感。维护工程师、资产经理和政策制定者可以利用所提出的方法来减少停机时间以及持续运营的风险。案例研究的结果证明，如果 PTG 和泵在运行中无人看管，它们是故障最敏感的部件，平均故障发生次数为 67 次和 45 次；而 THT 管道和 THT 罐在其生命周期中几乎平均 5 次被考虑进行重大维护请求时不太敏感。维护工程师、资产经理和政策制定者可以利用所提出的方法来减少停机时间以及持续运营的风险。