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AI-based Decision Support System for Heritage Aircraft Corrosion Prevention

Michal Kuchař, Jaromír Fišer, Cyril Oswald, Tomáš Vyhlídal

TL;DR

The paper tackles corrosion prevention for heritage aircraft housed in sheltered facilities by introducing a smart, data-driven DSS (SmartHangar) that fuses outdoor and indoor environmental data with material-specific degradation knowledge. It employs a retrainable MIMO decision-tree framework, supported by a software stack including PostgreSQL, FastAPI, PyCaret, Scikit-learn, and a Vue.js HMI, to generate actionable preservation recommendations. Key innovations include multi-material heritage protection, time-sensitive feature engineering (e.g., Time of Wetness), and ISO 9223-aligned risk assessment integrated with practical hangar considerations. The approach is demonstrated on WWII aircraft at the Aviation Museum Kbely to show how measured data translate into risk scores and concrete mitigation actions, offering a scalable preventive tool for museums and storage facilities.

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.

AI-based Decision Support System for Heritage Aircraft Corrosion Prevention

TL;DR

The paper tackles corrosion prevention for heritage aircraft housed in sheltered facilities by introducing a smart, data-driven DSS (SmartHangar) that fuses outdoor and indoor environmental data with material-specific degradation knowledge. It employs a retrainable MIMO decision-tree framework, supported by a software stack including PostgreSQL, FastAPI, PyCaret, Scikit-learn, and a Vue.js HMI, to generate actionable preservation recommendations. Key innovations include multi-material heritage protection, time-sensitive feature engineering (e.g., Time of Wetness), and ISO 9223-aligned risk assessment integrated with practical hangar considerations. The approach is demonstrated on WWII aircraft at the Aviation Museum Kbely to show how measured data translate into risk scores and concrete mitigation actions, offering a scalable preventive tool for museums and storage facilities.

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.

Paper Structure

This paper contains 7 sections, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Design and implementation of decision support system
  3. Used technologies
  4. Preprocessing
  5. Calculated features
  6. Risk evaluation
  7. Use case

Figures (4)

  • Figure 1: Architecture of decision support system SmartHangar
  • Figure 2: Measured one-year data and corrosion risk score evaluated.
  • Figure 3: One-year pollution data measured from weather stations in Prague-Holesovice and Prague-Riegrovy sady and indoor pollution calculated for min-max air exchange rate - Daily averages Kuchar.
  • Figure 4: Screenshot of the results part in HMI of SmartHangar.