AI-based Decision Support System for Heritage Aircraft Corrosion Prevention

TL;DR

This paper introduces a knowledge-based decision support system tailored for the long-term preservation of heritage aircraft, integrating corrosion prediction models for diverse materials and customized for museum environments.

Contribution

The paper presents a novel DSS that supports multi-material heritage protection and is specifically tailored to aircraft exhibition and storage conditions.

Findings

Successfully tested on WWII aircraft at Czech museum.

Supports corrosion prediction for aluminum, wood, and fabrics.

Enhances heritage preservation through tailored decision support.

Abstract

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.