Dynamic Risk-Based Maintenance for Offshore Processing Facility Jyoti Bhandari, a Ehsan Arzaghi, a Rouzbeh Abbassi, a Vikram Garaniya, a and Faisal Khan a,b a National Centre of Maritime Engineering and Hydrodynamic, (NCMEH), Australian Maritime College, University of Tasmania, Launceston, Tasmania, Australia; Rouzbeh.Abbassi@utas.edu.au (for correspondence) b Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada Published online 00 Month 2016 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/prs.11829 Processing facilities in a marine environment may not remain safe and available if they are not well maintained. Dynamic risk-based maintenance (RBM) methodology is a tool for maintenance planning and decision making, used to enhance the safety and availability of the equipment. It also assists in identifying and prioritizing the maintenance of equipment based on the level of risk. This article discusses an advanced methodology for the design of an optimum mainte- nance program integrating a dynamic risk-based approach with a maintenance optimization technique. In this study, Bayesian Network (BN) is employed to develop a new dynamic RBM methodology that is capable of using accident precursor information in order to revise the risk profile. The use of this methodology is based on its failure prediction capability which optimizes the cost of maintenance. The developed methodology is applied to a case study involving a failure of a separator system in the offshore oil and gas pro- duction platform considering marine environments. The result shows it is essential that the valve system in the separa- tor needs to be planned for maintenance once every 25 days; however, the cooler system can be planned for repairs once only biennially. A sensitivity analysis is also conducted to study the criticality of the operating system. V C 2016 American Institute of Chemical Engineers Process Saf Prog 000: 000–000, 2016 Keywords: reliability; dynamic risk-based maintenance; Bayesian network; separator; offshore production INTRODUCTION Plant safety in the process industry is directly aligned to the reliability of its operations. Higher reliability of the pro- cess plant can be achieved through a robust inspection and maintenance program. The main objective of the mainte- nance process is to make use of the knowledge acquired from failures and accidents so as to achieve the highest pos- sible standards in safety with the lowest possible cost. Over the past few decades, maintenance strategies have pro- gressed from primitive breakdown maintenance toward more sophisticated strategies like condition monitoring and reli- ability centered maintenance [1–3]. Risk-based maintenance (RBM) methodology provides a tool for maintenance plan- ning and decision making in order to reduce the equipment’s failure probability and, most importantly, limit the conse- quences of any failure [4]. To develop an appropriate mainte- nance strategy, it is necessary to estimate the impact of maintenance on assets and to determine the relationship between likelihood of undesirable events and the possible consequences. The most effective tool for estimating the like- lihood of hazard and associated consequences is probabilis- tic risk assessment [5]. Several researchers have demonstrated the application of a RBM strategy [2,3,6–12].In the beginning of the twentieth century, the American Society of Mechanical Engineers focused on performance criteria to improve safety and reduce the frequency of failure [10]. Later, the impor- tance of risk was recognized as a significant measure of sys- tems safety. Hagemeijer and Kerkveld [8] developed a methodology for risk-based inspections of pressurized sys- tems. The methodology of risk assessment was based on evaluating the likelihood of equipment failure and the rele- vant consequences. Harnly [9] developed a risk ranking inspection procedure that is used in one of Exxon’s chemical plants which prioritizes the repairs that have been identified during equipment inspection. Dey [7] presented a risk-based model for the inspection and maintenance of a petroleum pipeline; this model reduces the amount of time spent on inspections. The author of this study used Analytic Hierarchy Process, a multiple attribute decision-making technique, to identify the factors that influence the failures for the devel- oped risk-based model. Khan and Haddara [11] developed a comprehensive methodology for risk-based inspection and maintenance; it integrated a quantitative risk assessment and evaluation method with fault tree analysis (FTA). This meth- odology was applied to the case study of an ethylene oxide production facility. In their study a reverse fault tree was used to determine the optimal maintenance intervals [2]. Recently, researchers [3,10] reported the significance of applying a RBM strategy in process industries. Most of the aforementioned researches have used the FTA to determine the potential failure scenarios and their associ- ated probabilities. The well-known FTA and event tree (ET) approach is widely applied to identify the likelihood and consequences of accident scenarios in which, following an accident initiation, a number of safety barriers must fail before severe consequences arise [13]. In the past few years, the FT/ET approach has gained widespread acceptance as being a mature methodology for analyzing accident V C 2016 American Institute of Chemical Engineers Process Safety Progress (Vol.00, No.00) Month 2016 1