Abstract
Health monitoring of remote critical infrastructure is a complex and expensive activity due to the limited infrastructure accessibility. Inspection drones are ubiquitous assets that enhance the reliability of critical infrastructures through improved accessibility. However, due to the harsh operation environment, it is crucial to monitor their health to ensure successful inspection operations. The battery is a key component that determines the overall reliability of the inspection drones and, with an appropriate health management approach, contributes to reliable and robust inspections. In this context, this paper presents a novel hybrid probabilistic approach for battery end-of-discharge (EOD) voltage prediction of Li-Po batteries. The hybridization is achieved in an error-correction configuration, which combines physics-based discharge and probabilistic error-correction models to quantify the aleatoric and epistemic uncertainty. The performance of the hybrid probabilistic methodology was empirically evaluated on a dataset comprising EOD voltage under varying load conditions. The dataset was obtained from real inspection drones operated on different flights, focused on offshore wind turbine inspections. The proposed approach has been tested with different probabilistic methods and demonstrates 14.8% improved performance in probabilistic accuracy compared to the best probabilistic method. In addition, aleatoric and epistemic uncertainties provide robust estimations to enhance the diagnosis of battery health-states.
Abstract (translated)
远程关键基础设施的健康监测是一个复杂且昂贵的活动，由于基础设施的可访问性有限。检查无人机是一种无处不在的资产，通过提高可访问性来增强关键基础设施的可靠性。然而，由于恶劣的操作环境，监测它们的健康状况对于确保成功的检查操作至关重要。电池是关键组件，决定了检查无人机的整体可靠性，通过适当的 Health 管理方法，还提高了可靠且健壮的检查。在这种情况下，本文介绍了一种新颖的混合概率方法，用于预测锂-聚合物（Li-Po）电池的放电（EOD）电压。混合是在错误纠正配置下完成的，该配置将基于物理模型进行放电和概率误差纠正模型，以量化随机和知识不确定性。在各种负载条件下对 EOD 电压的性能进行了实证评估。数据集是从不同航班上运行的现实检查无人机获得的，重点关注海上风能涡轮机检查。所提出的方法已经通过不同概率方法进行了测试，与最佳概率方法相比，概率准确度提高了 14.8%。此外，随机和知识不确定性为提高电池健康状况的诊断提供了稳健的估计。
URL
https://arxiv.org/abs/2405.00055
