基于AI的系统可智能预测古董飞机多材料腐蚀，助力博物馆长期保护。
AI-based Decision Support System for Heritage Aircraft Corrosion Prevention
- 构建知识库，融合木材损伤模型与铝材腐蚀预测机制。
- 在捷克航空博物馆实测，对二战战机保护效果显著。
- 专为飞机展存仓环境定制，适配博物馆运维需求。
本文提出一种面向机库或展馆内保存的航空遗产长期保护的决策支持系统（DSS）。航空遗产由多种材料构成，包括古代铝合金、(夹)木结构及特殊织物。该DSS基于概念模型构建，其知识库涵盖主要材料的退化/腐蚀机理。针对历史木质部件，采用此前欧洲项目中开发的损伤函数模型；对于古老铝合金，则引入基于模型的腐蚀预测方法。该DSS的创新之处在于支持多材料遗产保护，并针对飞机展览/储存机库的特殊环境与航空博物馆的实际需求进行定制化设计。系统在捷克布拉格军事历史研究所航空博物馆展出的二战时期飞机遗产上进行了测试验证。
原文摘要 · Abstract (English)
The paper presents a decision support system for the long-term preservation of aeronautical heritage exhibited/stored in sheltered sites. The aeronautical heritage is characterized by diverse materials of which this heritage is constituted. Heritage aircraft are made of ancient aluminum alloys, (ply)wood, and particularly fabrics. The decision support system (DSS) designed, starting from a conceptual model, is knowledge-based on degradation/corrosion mechanisms of prevailing materials of aeronautical heritage. In the case of historical aircraft wooden parts, this knowledge base is filled in by the damage function models developed within former European projects. Model-based corrosion prediction is implemented within the new DSS for ancient aluminum alloys. The novelty of this DSS consists of supporting multi-material heritage protection and tailoring to peculiarities of aircraft exhibition/storage hangars and the needs of aviation museums. The novel DSS is tested on WWII aircraft heritage exhibited in the Aviation Museum Kbely, Military History Institute Prague, Czech Republic.
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