🤖 AI Summary
This study addresses the limited adaptability of conventional ship ballast water systems under hydraulic anomalies—such as valve failures or pipe blockages—and their heavy reliance on dense sensor arrays for fault diagnosis. The authors propose a novel approach that integrates graph theory with deep reinforcement learning, modeling ballast routing as a set of 54 feasible fluid transfer paths. By employing frame-stacked water level observations and action outcomes to approximate a partially observable environment, the method incorporates failure-action memory and dynamic action masking to enable adaptive rerouting. Notably, it implicitly infers blockage states without explicit high-dimensional POMDP modeling and introduces a fault-history scoring mechanism reliant only on sparse sensing to rank suspect components. Experimental results demonstrate 100% task success across all single-point blockage scenarios, reducing average decision steps from 61.0 to 41.5; the fault-scoring mechanism achieves 100% top-3 hit rate, with strict and inclusive top-1 hit rates of 66.7% and 83.3%, respectively.
📝 Abstract
Under the Shipping 4.0 paradigm, autonomous and reduced-crew vessels require intelligent internal systems to maintain operational safety and structural stability. Ballast-water control is essential for ship trim and integrity, but conventional rule-based or manual approaches have limited adaptability to hydraulic anomalies such as valve failures and pipe blockages, and often depend on dense pressure or flow sensors for diagnosis. To address these limitations, this paper proposes RL-Ballast, a graph-based deep reinforcement learning framework for adaptive ballast-water path planning and sensor-frugal blockage candidate scoring. The valve-permutation problem is transformed into 54 feasible fluid-transfer routes generated using graph theory and depth-first search. The partially observable ballast environment is approximated with frame-stacked tank levels and action outcomes, allowing the agent to infer hidden blockage effects without explicitly modeling a high-dimensional POMDP. During deterministic inference, episode-level failed-action memory and dynamic action masking prevent repeated ineffective actions and support immediate rerouting. Failed transfer histories are further accumulated to rank suspicious valves or pipe segments without dense instrumentation. Monte Carlo simulations show that RL-Ballast completes all unexpected single-blockage scenarios and reduces average decision steps from 61.0 to 41.5 compared with a Dijkstra rule-based baseline. For diagnostic support, the failure-history scoring scheme achieves a 100% Top-3 hit rate, a 66.7% strict Top-1 hit rate, and an 83.3% Top-1 tie-hit rate under serially indistinguishable blockage conditions. These results suggest that RL-Ballast enables adaptive rerouting and maintenance-oriented blockage diagnosis under limited sensing conditions.