New Conference Paper Published

A new conference paper co-authored by Enrico Fraccaroli has been published in the proceedings of the 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN).

• Enrico Fraccaroli

We are excited to announce the publication of our latest conference paper, titled “Fault Injection for Synthetic Data Generation in Aircraft: A Simulation-Based Approach”, in the proceedings of the 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN).

Abstract

The safety of aircraft heavily depends on the integrity of the Landing Gear System (LGS). However, gathering real-world fault data to support effective Prognostic and Health Management (PHM) practices presents significant challenges. This work proposes a novel methodology for generating synthetic fault data using a multi-physics Simscape model of a landing gear deployment/retraction mechanism. The model incorporates specialized fault blocks designed to replicate various hydraulic failure modes, aiming to broaden the pool of fault data covering the most common failures. This approach promises to enhance maintenance strategies and facilitate the development of hybrid Model-Based and Data-Driven solutions. Ultimately, the results of this study will be used to understand the physics within the landing gear better and gather the necessary data to create an effective Digital Twin for predictive maintenance.

Details

  • Title: Fault Injection for Synthetic Data Generation in Aircraft: A Simulation-Based Approach
  • Authors: Francesco Biondani, Nicola Dall’Ora, Francesco Tosoni, Enrico Fraccaroli, Domenico Fabio Migliore, Francesco Acerra, Franco Fummi
  • Conference: 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN)
  • Year: 2024
  • Pages: 1-8
  • Keywords: Landing Gear System; Fault Injection; Synthetic Data Generation; Prognostic and Health Management; Digital Twin; Multi-Physics Simulation; Hydraulic Failures; Aircraft Maintenance; Model-Based Solutions; Data-Driven Solutions

This paper proposes methods to generate synthetic fault data for landing gear systems to support PHM and digital twin creation. We extend our gratitude to all collaborators and contributors for their efforts.

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