System resilience characterizes the capability of maintaining the required functionality under disruptions, which is of great significance in evaluating the productivity and safety of complex engineered systems. Although most studies conduct resilience assessment from qualitative and quantitative perspectives, system functionality that reflects functional requirements for complex engineered systems needs to be elaborated. In addition, given that complex engineered systems achieve dynamic performance during disruptions, measuring the actual performance under uncertainty is imperative. To this end, this paper develops a quantitative framework to assess the resilience of complex engineered systems. The developed framework comprises three phases, functionality analysis, performance evaluation, and resilience assessment. Firstly, system functionality is analyzed using a functional tree illustrating the relationship between functions. The overall objective, primary functions, and sub-functions are identified according to task requirements. Secondly, system performance is quantified considering uncertain factors through Graphical Evaluation and Review Technique (GERT). Probabilistic branches and network logic are employed to represent the implementation of various functions. Finally, resilience assessment is carried out from the perspectives of anticipation, absorption, adaptation, and restoration abilities. A case study on the satellite network shows the effectiveness of the developed framework. The developed framework determines system functionality based on task requirements, evaluates system performance with limited information, and accurately assesses system resilience.@en

Recent years have seen the increasing complexity of engineered systems. Complexity and uncertainty also exist in engineered systems’ interactions with human operators, managers, and the organization. Resilience, focusing on a system's ability to anticipate, absorb, adapt to, and recover from disruptive situations, can provide an umbrella concept that covers reliability and risk-based thinking to ensure these complex systems' safety. This paper discusses the quantitative aspects of the notion of resilience. Like the quantitative risk assessment framework, a generic framework should be developed for quantitative resilience assessment. This paper proposes a framework based on a triplet resilience definition consisting of disruption, functionality, and performance. Uncertainty treatment is also considered. The proposed framework aims to answer the question of “resilience of what to what” and how it can be quantitively assessed.@en

Complex engineered systems with various components and dynamic behaviors are connerstones to develop resilient cities and societies. These systems are robust but also vulnerable to adverse events and inevitably suffer performance degradation. An immediate question would be, “how can we manage and improve the resilience of a complex engineered system?”. This study proposes a quantitative framework to assess the resilience of complex engineered systems. The proposed framework focuses on figuring out the impact of functionality implementation on the capability of complex engineered systems to anticipate, absorb, adapt to, and restore from disruptive events. It is composed of three parts, including functionality analysis, performance evaluation, and resilience measure. Firstly, various functions are analyzed at the system level, where a functional tree is employed to investigate the relationship between functions. Then the actual performance of the system is evaluated while uncertain implementation of system functionality is considered. Finally, system resilience is measured from the perspectives of anticipation, absorption, adaptation, and restoration. Anticipation, absorption, adaptation, and restoration are critical capacities of complex engineered systems to ensure normal operation in the event of disruptions. The proposed framework provides a general approach for resilience assessment of complex engineered systems, which figures out functionality implementation and system performance under uncertainty.@en

When designing the maintenance of multi-component aircraft systems, we consider parameters such as safety margins (used when component replacements are scheduled), and reliability thresholds (used to define data-driven Remaining-Useful-Life prognostics of components). We propose Gaussian process learning and novel adaptive sampling techniques to efficiently optimize these design parameters. We illustrate our approach for aircraft landing gear bakes. Data-driven, Remaining-Useful-Life prognostics for brakes are obtained using a Bayesian linear regression. Pareto optimal safety margins for scheduling brake replacements are identified, together with Pareto optimal reliability thresholds for prognostics.@en