Model-Based Resilience Assessment Framework for Autonomous Systems

Abstract

Abstract While automation technologies advance faster than ever, gaps of resilience capabilities between autonomous and human-operated systems have not yet been identified and addressed appropriately. To date, there exists no generic framework for resilience assessment that is applicable to a broad spectrum of domains or able to take into account the impacts on mission-scenario-level resilience from system-specific attributes. In the proposed framework, resilience is meant to describe the ability of a system, in an open range of adverse scenarios, to maintain normal operating conditions or to recover from degraded or failed states in order to provide anticipated functions or services to achieve mission success. The term resilience is introduced in relation with classical terms such as fault, error, failure, fault-tolerance, reliability, and risk. The proposed model-based resilience assessment framework is based on a resilience ontology that enables the use of system models into reliability and risk models for transparent, persistent, and up-to-date modeling and quantification. A SysML profile and associated OWL ontology are defined to enable the use of a range of resilience mechanisms into the design and operation of a system.