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DigiMaTRIA is an innovative project that combines robotics, IoT sensor technologies, and artificial intelligence to transform the maintenance of the external envelope of industrial assets. Using a digital twin approach, the project enables continuous monitoring, fault prediction, and informed decision-making—reducing costs, increasing asset lifespan, and contributing to environmental sustainability.
Highlights
Integrated multisensory monitoring
Inspection with UAVs (drones)
Predictive analytics with AI
Reduction in inspection costs
Reducing the carbon footprint
About
We are an innovation consortium comprised of Garcia, Garcia SA, Gar.com, ISEP, and INESC TEC, uniting industry and academia to develop advanced digital solutions for the industrial maintenance sector.
Mission
Transforming industrial maintenance management through robust and intelligent digital solutions.
Vision
To be a global leader in predictive maintenance of industrial assets based on Digital Twins.
Deliverables
E4.4
Service platform
(beta version and final version)
2027-10_E.4.4.a
2028-02_E.4.4.b
E5.1
Reports with the results of tests and verifications of DigiMaTRIA services.
2027-07_ E5.1
E5.2
Results of the DigiMaTRIA service demonstration and validation.
2028-02_ E5.2
E6.1
E6.2
Summary reports on the progress of dissemination and promotion activities.
2027-03_E.6.2.a
2028-03_E.6.2.b
TEAM
Rui Salgado
IT at Gar.Com
Ruben Silva
R&D at Garcia Garcia, SA
Diogo Ribeiro
Professor at ISEP
André Dias
Robotics at INESC-TEC
Milestones & Resources
The DigiMaTRIA project runs from March 2025 to March 2028, with an eligible investment exceeding 980,000 euros. It is structured around seven main activities, from defining requirements to validating and disseminating the solution.
2025
Project initiation and requirements definition (W1)
Establish the control variables for the degradation status of assets and the functional requirements of the DigiMaTRIA system.
2026
Development and integration of technologies (W2–W4)
Creation of UAV systems and static sensors for acquiring multisensory data from the external environment of assets.
2027
Testing, validation and dissemination (W5–W6)
Develop AI models for anomaly detection, risk classification, and degradation prediction.