Traditional maintenance often relies on manual inspections and scheduled check-ups. While these are useful, they can be time-consuming, prone to human error, and they risk to miss the early signs of potential failures. As a result, organizations are increasingly turning to advanced technologies to implement proactive maintenance strategies.
The use of AI unlocks significant potential for companies in the pharmaceutical and healthcare industries. In this article, we introduce some typical and particularly worthwhile areas of application.
The RoboTUNN research project is revolutionizing subway tunnel inspections by using robotics and artificial intelligence. Its goal is to autonomously detect damages and create digital twins that enable predictive maintenance. StatSoft plays a key role in data integration and the development of AI models for predictive maintenance. The project is funded by the mFUND initiative.
In industrial manufacturing, the analysis of image data enhances monitoring and optimizes defect detection. Standardized processes allow the use of machine learning (ML) to handle complex image signals. For more challenging tasks, AI models like Convolutional Neural Networks (CNN) offer more effective solutions. Discover how pre-trained models boost efficiency and the benefits this technology brings to real-world applications.