Abstract
Unmanned engineering systems that execute various operations are becoming increasingly complex relying on a large number of components and their interactions. The reliability, maintainability, and performance optimization of these systems are critical due to their intricate nature and inaccessibility during operations. This paper introduces a new reliability-based optimization framework for planning operational profiles for unmanned systems. The proposed method employs deep learning techniques for subsystem health monitoring, dynamic Bayesian networks for system reliability analysis, and Bayesian optimization schemes for optimizing system performance. The proposed framework systematically integrates these schemes to enable their application to a wide range of tasks, including offline reliability-based optimization of system operational profiles. This framework is the first in the literature that incorporates health monitoring of multi-component systems with causal relationships. Using this hybrid scheme on unmanned systems can improve their reliability, extend their lifespan, and enable them to execute more challenging missions. In order to demonstrate the proposed approach, the optimization strategy is implemented and executed using a simulation model for the engine cooling and control system of an unmanned surface vessel.
