Risk management is a fundamental approach to improving critical infrastructure systems’ safety against disruptive events. This approach focuses on designing robust critical infrastructure systems (CISs) that could resist disruptive events by minimizing the possible events’ probability and consequences using preventive and protective programs. However, recent disasters like COVID-19 have shown that most CISs cannot stand against all potential disruptions. Recently there is a transition from robust design to resilience design of CISs, increasing the focus on preparedness, response, and recovery. Resilient CISs withstand most of the internal and external shocks, and if they fail, they can bounce back to the operational phase as soon as possible using minimum resources. Moreover, in resilient CISs, early warning enables managers to get timely information about the proximity and development of distributions. An understanding of the concept of resilience, its influential factors, and available evaluation and analyzing tools are required to have effective resilience management. Moreover, it is important to highlight the current gaps. Technological resilience is a new concept associated with some ambiguity around its definition, its terms, and its applications. Hence, using the concept of resilience without understanding these variations may lead to ineffective pre- and post-disruption planning. A well-established systematic literature review can provide a deep understanding regarding the concept of resilience, its limitation, and applications. The aim of this paper is to conduct a systematic literature review to study the current research around technological CISs’ resilience. In the review, 192 primary studies published between 2003 and 2020 are reviewed. Based on the results, the concept of resilience has gradually found its place among researchers since 2003, and the number of related studies has grown significantly. It emerges from the review that a CIS can be considered as resilient if it has (i) the ability to imagine what to expect, (ii) the ability to protect and resist a disruption, (iii) the ability to absorb the adverse effects of disruption, (iv) the ability to adapt to new conditions and changes caused by disruption, and (v) the ability to recover the CIS’s normal performance level after a disruption. It was shown that robustness is the most frequent resilience contributing factor among the reviewed primary studies. Resilience analysis approaches can be classified into four main groups: empirical, simulation, index-based, and qualitative approaches. Simulation approaches, as dominant models, mostly study real case studies, while empirical methods, specifically those that are deterministic, are built based on many assumptions that are difficult to justify in many cases.

The development of offshore energy resources involves highly complex and extensive technological processes. Reliability evaluation of offshore production facilities provides essential information in the design and operation phase. Historical reliability data play an important role in reliability analysis, and as such data reflect the effect of influencing factors that production facilities have experienced during their life cycle. Due to there being less offshore activity in the Arctic region compared with other areas, there is a lack of data and little experience available regarding operational equipment. In contrast to the Arctic region, oil and gas companies have a lot of experience and information related to the design and operation of offshore production facilities in the other parts of the world. Using this type of data and information, collected from similar systems but under different operational conditions, in design processes for the Arctic region may lead to incorrect design. This may increase health, safety, and environmental (HSE) risk or operating and maintenance costs. This paper develops a methodology for the application of the accelerated failure time model (AFT) to predict the reliability of equipment to be used in the Arctic region based on the available data. In the methodology used here, the available data is assumed to reflect the behavior of the equipment under low stress conditions, and using the AFT models the reliability of equipment in the Arctic environment, which represents high stress, is predicted. An illustrative example is used to demonstrate how the methodology can be applied in a real case.