Predictive Maintenance and smart operations

Many sectors aim for a reliable and safe use of equipment, machinery and other infrastructures. How can a wind farm safely generate maximum energy at minimum costs? When should the government perform maintenance on a bridge to ensure safety? How can a manufacturer ensure automatic adjustment of high-tech instruments? TNO tries to answer these questions. By using AI, we facilitate an improved planning of predictive maintenance and reliable operations.

Work with us in APPL.AI

Contact us about Smart operations and predictive maintenance


using AI for Predictive maintenance and reliable operations

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.

Dealing with data characteristics and confidentiality

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.

What does TNO offer?

  • We help organisations develop AI applications, revealing its opportunities and pitfalls.
  • We develop AI workflows for decision support in energy production and transport (road, rail, water, energy) in close collaboration with industrial partners and authorities.
  • We make a digital twin of structures, machines or systems by combining physics-based modelling skills, domain knowledge and data analytics/artificial intelligence techniques.
  • We develop system solutions that integrate innovations in computer vision, robotics, machine learning and human-machine teaming.
  • Thanks to our experience in sensor-data collection, (OPC-UA) data communication, processing and storage in Digital Twin data platform, we can manage data to train your AI algorithm.
Application areas