Abstract
Resilience engineering brings a new perspective for managing safety against unexpected and unpredictable disturbances. Many engineering systems require coordinating automation and human elements for achieving safe and resilient operations. This paper emphasizes the distinct essence of short-term human-automation resilience and proposes a systemic assessment methodology based on the functional resonance analysis method (FRAM). In this methodology, a multi-layer FRAM based modeling approach is developed to capture the emergent property of short-term resilience manifested from human-automation interaction and cooperation. An improved bidirectional algorithm is integrated with Monte Carlo simulation for numerical modeling of performance variability across the multi-layer FRAM model and thus quantifying resilience using a probabilistic variability-based metric. A case study is presented for applying the proposed methodology to the dynamic positioning system in the offshore tandem offloading process. The results show that it is useful to assess short-term human-automation resilience in different scenarios and guide decision-making and countermeasures for enhancing system resilience.
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