This study investigates the impact of ozone exposure on the hardness of synthetic rubber specimens (a blend of NR (natural rubber) and CR (chloroprene rubber)) through accelerated aging tests. Using a linear regression model, the research predicts the lifespan of rubber under real-world conditions and demonstrates how established experimental methods can yield novel insights when applied to synthetic rubber. The results show that ozone exposure significantly increases hardness within the first 10 days, stabilizing after day 12. Through analysis, this study calculates acceleration factors based on ozone concentration and temperature, estimating the practical lifespan of synthetic rubber under actual conditions to be approximately 25.76 years. These findings provide valuable indicators for evaluating the durability of synthetic rubber materials and predicting the longevity of rubber products in industrial applications. Furthermore, the research emphasizes the potential for improving lifespan prediction accuracy by incorporating non-linear models or machine learning approaches.

The impact of the Fourth Industrial Revolution has brought significant attention to Condition-based maintenance (CBM) for autonomous ships. This study aims to apply CBM to the fuel supply pump of a ship. Five major failures were identified through reliability analysis, and structural analysis was conducted to investigate the mechanisms by which one failure induces another, leading to the identification of three compound failure scenarios. Data were collected on a test bed under normal conditions, five single failure conditions, and three compound failure conditions. The acceleration data from the experiments were transformed into 2D arrays corresponding to a single pump rotation, and a method was proposed to compensate for the errors accumulated during the repeated array generation. The data were vectorized using a simplified CNN structure and applied to six multi-label learning methods, which were compared to identify the optimal approach. Among the six methods, the Label Powerset (LP) was found to be the most effective. Multi-label learning captures correlations between labels, similar to the failure-inducing mechanisms learned from structural analysis.

The fourth Industrial Revolution is driving the maritime and port logistics industry toward greater operational efficiency and reduced human intervention through automatic mooring systems. This study developed and applied a lab-based evaluation procedure to assess the performance and stability of vacuum suction pads in such systems. Test conditions were based on Cavotec’s specifications, with evaluation criteria tailored to the pads’ required performance. Relevant standards were reviewed to establish a vacuum pressure leakage criterion of 5% or less. Results showed that the pads maintained pressure reductions below this threshold, confirming their suitability for automatic mooring, though dynamic factors like heave and tides remain untested.