Ing MSc Internship Proposal in Computer Science Pau Data-Driven Anomaly Prediction for Reducing Energy Consumption in the Low-Carbon Aircraft Production Chain

Joining LINEACT at CESI for a research internship would be a fantastic opportunity to contribute to innovative projects while deepening my skills in a cuttingedge environment focused in Computer Science

Abstract

The production chain of lowcarbon aircraft relies on highprecision industrial equipment to manufacture and assemble the various aircraft components. The proper functioning of these production machines is essential to ensure efficient manufacturing minimize downtime and optimize energy consumption. However unexpected anomalies and failures can lead to costly disruptions and excessive energy use. This internship focuses on developing an anomaly prediction system for aircraft parts production machines leveraging big data and artificial intelligence (AI). The goal is to analyze data from industrial sensors to detect weak signals indicating potential malfunctions predict failures and optimize maintenance strategies.

Keywords: Failure prediction Predictive maintenance Machine learning Optimization

Research Work

Scientific Fields:

Machine Learning / Deep Learning Big Data analysis and anomaly detection predictive maintenance.

Work Program/Objectives:

of a data collection and integration platform to aggregate realtime sensor data from production machines ensuring a comprehensive view of operational parameters.

of machine learningbased predictive maintenance models to anticipate failures optimize maintenance schedules and assess their impact on production efficiency and energy consumption.

of reliability and accuracy metrics to evaluate the effectiveness of predictive models in minimizing downtime and improving operational performance.

and validation of predictive strategies in realworld manufacturing conditions to test model robustness and enhance energy efficiency.

Prior works in the laboratory

As part of the LINEACT laboratory several research initiatives have been conducted in the field of predictive maintenance with a particular focus on applying artificial intelligence and metaheuristics to optimize the management of complex systems. Marwa DAAJI proposed an innovative approach to failure prediction in cyberphysical systems notably for wind turbines by framing the problem as a multiobjective optimization task and applying evolutionary algorithms (Daaji et al. 2023. Her research demonstrated the effectiveness of her methods validated with realworld data to anticipate defects and improve system reliability. On the other hand Ghita BENCHEIKH developed a multiagent system for the joint scheduling of production tasks and predictive maintenance optimizing resource usage while considering the health status of machines (Bencheikh 2022; Bencheikh & Bettayeb 2024. She also designed failure prediction models for industrial machines using machine learning techniques enabling proactive maintenance and reducing unexpected breakdowns (Maataoui et al. 2023.

Expected scientific/technical production

A comprehensive review of existing predictive maintenance methods specifically applied to manufacturing machines used in aircraft parts production.

A cleaned and wellorganized dataset of sensor data from production machines ready for machine learning modeling to predict failures and optimize maintenance.

A machine learning model to predict potential failures with a detailed explanation of the methodology and techniques used.

An evaluation of the predictive models performance including key metrics and analysis of its ability to reduce downtime and improve energy efficiency in production.

A userfriendly dashboard to visualize machine performance realtime anomalies and predictive maintenance insights for better decisionmaking in the production environment.

Context

Lab presentation

CESI LINEACT (UR 7527 Laboratory for Digital Innovation for Businesses and Learning to Support the Competitiveness of Territories anticipates and accompanies the technological mutations of sectors and services related to industry and construction. The historical proximity of CESI with companies is a determining element for our research activities. It has led us to focus our efforts on applied research close to companies and in partnership with them. A humancentered approach coupled with the use of technologies as well as territorial networking and links with training have enabled the construction of crosscutting research; it puts humans their needs and their uses at the center of its issues and addresses the technological angle through these contributions.

Its research is organized according to two interdisciplinary scientific teams and several application areas.

Team 1 Learning and Innovating mainly concerns Cognitive Sciences Social Sciences and Management Sciences Training Techniques and those of Innovation. The main scientific objectives are the understanding of the effects of the environment and more particularly of situations instrumented by technical objects (platforms prototyping workshops immersive systems... on learning creativity and innovation processes.

Team 2 Engineering and Digital Tools mainly concerns Digital Sciences and Engineering. The main scientific objectives focus on modeling simulation optimization and data analysis of cyber physical systems. Research work also focuses on decision support tools and on the study of humansystem interactions in particular through digital twins coupled with virtual or augmented environments.

These two teams develop and cross their research in application areas such as

Industry 5.0

Construction 4.0 and Sustainable City

Digital Services.

Areas supported by research platforms mainly that in Rouen dedicated to Factory 5.0 and those in Nanterre dedicated to Factory 5.0 and Construction 4.0.

Links to the research axes of the research team involved

This internship topic falls within the Management and Decision and Predictive Maintenance focus areas of Team 2 Engineering and Digital Tools.

Presentation of C2A project

Supported by state investment as part of the France 2030 Plan Campus Aero Adour (C2A) is a project to support the digital and environmental transition of the aeronautics industry in the Adour territory. As a laureate of the AMI Comptences et Mtiers dAvenir call for projects under the Producing LowCarbon Aircraft strand C2A will benefit from State support through the France 2030 initiative over five years.