Mission Aborting in n-Unit Systems With Work Sharing

Mission aborting has recently attracted great attention, where mission abort rules (MARs) have been modeled and optimized for different types of technological systems aiming to effectively mitigate the risk of system losses. However, none of the existing works have considered systems with multiple work-sharing units. This article makes advancements in the state of the art by modeling condition-based MARs for nonrepairable work-sharing systems that must perform a specified amount of work during the primary mission (PM). The MAR considered presumes aborting the PM to prevent considerable damage when the number of available units reduces to a certain number k while the amount of work accomplished in the PM is less than L(k). After the PM abortion, a rescue procedure (RP) is executed by the remaining units to survive the system. Dynamic operating conditions during PM and RP are considered. A probabilistic model-based numerical algorithm is proposed to evaluate several performance metrics, including mission success probability, RP success probability, damage avoidance probability, and expected cost of losses. The MAR optimization problem for minimizing the expected cost of losses is formulated and solved using the genetic algorithm. An example of a chemical reactor system is provided to demonstrate the proposed algorithm as well as the benefit of MARs optimized in comparison to the ``no abort'' policy and the most conservative abort policy (the PM aborts upon the failure of any unit). Effects of several model parameters on the system performance metrics and optimization solutions are also examined through examples.

Automated summary

This article focuses on condition-based mission abort rules for nonrepairable work-sharing systems, proposing a probabilistic model algorithm to evaluate performance metrics and optimizing the mission abort rules using a genetic algorithm. The research is demonstrated through an example of a chemical reactor system, showing the application of the algorithm and the benefits of optimized MAR policies compared to the "no abort" policy and the most conservative abort policy.