SMART-MAINT

Research-driven pedagogical platform connecting IoT, real data, predictive maintenance and engineering education.

Overview

SMART-MAINT is a structuring pedagogical project dedicated to the development of an intelligent platform for predictive maintenance education.

The project aims to connect research and teaching by exposing students to real data, instrumented systems, IoT sensor networks and maintenance-oriented diagnostic reasoning.

The platform is designed within the Department of Industrial Engineering and Maintenance (GIM) at the IUT of Perpignan, Université de Perpignan Via Domitia.

Scientific and Pedagogical Context

Modern industrial maintenance increasingly relies on data acquisition, condition monitoring, predictive analytics and intelligent decision support.

However, students often encounter these topics through abstract examples or simplified datasets disconnected from real industrial constraints.

SMART-MAINT addresses this limitation by providing a real experimental platform where students can observe, acquire, process and interpret data from instrumented systems.

Main Objectives

The project pursues the following objectives:

  • Develop a pedagogical platform for predictive maintenance.
  • Integrate IoT sensors and industrial monitoring technologies.
  • Expose students to real data acquisition problems.
  • Introduce predictive maintenance and PHM concepts.
  • Connect maintenance education with artificial intelligence.
  • Promote project-based and data-driven learning.
  • Strengthen the link between research and teaching.

Methodology

The platform is organized around a complete maintenance-oriented data chain.

SMART-MAINT pedagogical platform connecting instrumentation, IoT data acquisition, monitoring, predictive maintenance and student learning activities.

Axis 1 – Instrumented Systems

The platform integrates real systems equipped with sensors for monitoring variables such as:

  • Temperature
  • Vibration
  • Electrical quantities
  • Environmental variables
  • Operational states

Axis 2 – IoT and Data Acquisition

Students interact with IoT-based acquisition systems using:

  • Distributed sensors
  • LoRa communication
  • Embedded devices
  • Databases
  • Dashboards
  • Real-time monitoring tools

Axis 3 – Predictive Maintenance

The collected data are used to introduce:

  • Fault detection
  • Trend analysis
  • Degradation monitoring
  • Maintenance indicators
  • Prognostics and Health Management concepts
  • Decision support

Axis 4 – Pedagogical Integration

SMART-MAINT supports teaching activities in:

  • Maintenance methods
  • Predictive maintenance
  • Instrumentation
  • Data analysis
  • Automation
  • Industrial monitoring

Educational Impact

SMART-MAINT helps students move from theoretical knowledge to practical reasoning by working with realistic data acquisition and maintenance problems.

The platform promotes:

  • Autonomy
  • Critical analysis
  • Experimental reasoning
  • Data interpretation
  • Engineering decision-making
  • Collaborative learning

Research-Teaching Connection

The project is directly connected to research activities on:

  • IoT sensor reliability
  • Predictive maintenance
  • Intelligent diagnostics
  • Data quality
  • Edge AI
  • Cyber-physical systems

It also provides a pedagogical bridge toward research projects such as INFINITY and ALARMES.

Expected Outcomes

The project is expected to deliver:

  • A permanent pedagogical platform.
  • Teaching resources for predictive maintenance.
  • Student projects based on real data.
  • Experimental activities for BUT GIM students.
  • Research-oriented pedagogical datasets.
  • Future collaborations with industrial and academic partners.

Keywords

Predictive Maintenance · Engineering Education · IoT · Instrumentation · Maintenance 4.0 · Maintenance 5.0 · Data Acquisition · PHM · Industrial Monitoring · Active Learning

Project Status

Ongoing