Smart operations and predictive maintenance
TNO uses AI to boost efficiency of operations and predictive maintenance in the three areas:
1. Energy production and transport systems: TNO develops data-driven models and optimisation routines to support strategic and operational decisions. This makes it possible to cope with a high level of uncertainty and complexity.
2. Predictive maintenance of structures: Inspecting infrastructures (e.g. bridges and production facilities) is complex, labour-intensive and requires human interpretation. Here, predictive maintenance is of great value for safety. This relies on intelligent digital twin technology to improve monitoring and maintenance planning and degradation assessment through automated damage-pattern recognition.
3. Manufacturing industry: There is an increasing demand for flexibility in the product mix. We therefore need to maintain a high quality control, while ensuring real-time and continuous monitoring. At TNO, we look at the total work flow and accessible data to determine the appropriate AI-driven solution. This can vary from dedicated quality sensor development, intelligent digital twin technology to a physics-based model supported by AI.
There is great potential for AI in smart operations and predictive maintenance. There are also challenges. Think of dealing with limited and poor quality data or ensuring confidentiality when sharing data between multiple parties. The application of AI calls for an understanding of the relevant domain requirements.
In many cases a combination of model-based and data-driven AI (hybrid AI and digital twins) can be used to deal with limitations to data. We exploit prior model knowledge by making use of what we already know to be true. We do this in collaboration with partners from the manufacturing industry, energy and construction sector. We also pay attention to transparent and trusted collaboration between AI system and operators.