JNI3 – Digital twins for predictive maintenance and health indicators
Project description
Monitoring the state of health of industrial systems.
Launched in 2022 for a 4-year period, the JNI3 project aims to develop and implement Digital Twins (JN – Jumeaux Numériques) to monitor the state of health of industrial systems. It is focuses on the definition of digital twins capable of collecting data in real time and exploiting it to assess the state of health of the industrial system. The developed digital twin will be able to predict and optimize the lifetime evolution of the system depending on the different health indicators identified.
The JNI3 project falls within the scope of the Digital twin of complex industrial systems programme, led by IRT SystemX.
Expected results
- Define relevant health indicators from data flows collected in real time/delayed time on the industrial asset
- Develop data selection methods to design a health model
- Develop a digital twin capable of predicting early signs of equipment deterioration and offer preventive measures
- Based on the prognosis of the industrial system on the availability of maintenance teams and production needs, develop a data-based digital twin to optimize under uncertainty the maintenance of intelligent industrial systems
Implemented skills
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Data science and AI |
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Optimization |
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Safety |
Targeted markets
- Aeronautics
- Energy
- Manufacturing industry
Supervised theses
- Thesis 1: Constrained predictive maintenance optimization under uncertainty
- Thesis 2: Health key indicators for predictive maintenance using digital twin
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