LAUSR

The use of average data for dependability assessments results in an outdated system-level dependability estimation, which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field, which provides asset-specific failure information that can be reused to improve the system-level failure estimation. This paper presents a framework for prognostics-updated dynamic dependability assessment. The dynamic behavior comes from runtime updated information, asset interdependencies, and time-dependent system behavior. A case study from the power generation industry is analyzed, and results confirm the validity of the approach for improved near real-time unavailability estimations.