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In the last decades, many researchers have directed their efforts towards the safety improvement of plants where hazardous substances are processed or handled. This attitude has led to developing strategic plans for minimizing risk and costs arising from the operations. Accidents related to hazardous substances can indeed pose a threat to human beings and the surrounding environment, therefore a reliable tool for engineering maintenance is required. This paper presents a comparison of two different Risk-Based Maintenance (RBM) approaches for prioritizing maintenance actions. The first approach consists of a classic Quantitative Risk Analysis (QRA), where standard probabilities from literature are exploited for the modeling of the different scenarios. In this study, the catastrophic rupture and three sizes of leakage have been chosen as reference scenarios for each component. The analysis is carried out through a software named Safeti (by Den Norske Veritas -- German Lloyds DNV-GL), which performs calculations based on standard source, dispersion and consequence models. Safeti provides a ranking of the components based on their criticalities. In the second technique, Hierarchical Bayesian Network (HBN) is adopted to estimate the probability of failure components, while the severity is assessed via Failure, Mode, Effects and Criticality Analysis (FMECA). Subsequently costs related to each component are evaluated and a Cost Risk Priority Number (CRPN) is obtained. This comprehensive review can help maintenance engineers to reduce risks resulting from operations and pinpoint the most critical components, by using the approach that is more suitable for their case. To demonstrate the two different approaches and compare their results a Natural Gas Regulating and Metering Station (NGRMS) is considered as case of study. The results show that applying the two methods to the same plant gives different component rankings, due to their different sensitivities and settings. [ABSTRACT FROM AUTHOR]

The likelihood of leaks from process equipment is a key input to any Quantitative Risk Assessment. This study is aimed at developing a methodology supporting the tailorization of leak frequency values. Specific modification factors for the facilities of the Oil & Gas (O&G) upstream sector are used for this purpose. The method (TEC2O--frequency modification methodology based on TEChnical Operational and Organizational factors) is based on an aggregated set of indicators, whose contribution to the expected leak frequency is systematically evaluated through a specific procedure. Periodic revision and update represents an added value to the method, because it may be adopted to drive the identification of critical safety issues in a facility, integrating technical and managerial aspects and supporting continuous monitoring. The method is compared with similar literature methods and applied to a representative case study in order to demonstrate its potential and advantages. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Applying Non-Homogenous Poisson Process (NHPP) and Homogenous Poisson Process (HPP) to predict the condition of a system. • Presenting the possible differentiation within failure assessment of random processes. • Investigating the effect of utilizing observed data in inter-arrival failure time modelling. • The model was examined on degradation process of the Natural Gas Regulating and Metering Stations (NGRMS). 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. [ABSTRACT FROM AUTHOR]