Air Fusion Sp. z o.o. (AF) is an EU-based subsidiary of a U.S. software company. As an end-user of the AI-SPRINT 36-month research and innovation action, Air Fusion brings innovations to inspection and maintenance by transforming the infrastructure inspection sector with is proprietary AI-machine learning for automated damage detection, classification and change detection.
Use case ecosystem
This use case combines the efforts of:
- Air Fusion: End-user developing a novel solution by optimally using more powerful resources in the performance of first-level image analysis at the edge and interventions triggered by edge processing using more powerful AI cloud-based systems.
- 7BULLS: Enhancing the monitoring platform and advanced scheduling solutions for accelerated devices for model training and retraining.
- Cloud & Heat: Providing the infrastructure and computing resources (including GPUs) for testing and validating the use case.
- Politecnico di Milano: Supporting the definition and optimisation of the AI models.
Wind turbines have a lifespan of around 20–25 years, according to research conducted by the Imperial College in London, but this lifespan can be drastically reduced when a turbine is damaged.
Wind power generator failure is often linked to the degradation of blades, generators and gearboxes and costs to fix damaged components can be extremely high, ranging from $300,000 to replace a blade to $5 million to repair the entire station.
Conducting a proper maintenance and inspection is crucial to reduce the risks of wind turbine breakage. This is where artificial intelligence systems come into play by processing massive amounts of data and using it to better predict and analyse when and what type the maintenance is needed over time.
The AI-SPRINT use case on predictive maintenance and inspection of wind turbines uses AI models for detecting damages through the collection of images by drones during their flight paths and sending them to the edge-cloud channel for analysis.
This is a crucial step in accelerating inspection time by drastically reducing the time spent by operators to analyse damage or maintenance requirements, as well as the likelihood of human error by using machine learning systems.
- Enable the best interaction of cloud-based analysis and local processing using lighter data pattern recognition routines.
- Increase the reliability of windmill plants and enable predictive maintenance.
- Exploit privacy preserving solutions, whenever a potential problem is detected.
This AI-SPRINT use case will significantly improve the efficiency of AI models, bringing new market opportunities for the entire damage identification workflow. Air Fusion will be able to take to market novel AI-enabled products, spanning telco towers, power transmission lines, gas pipelines and the energy footprint of buildings by using distributed AI facilities. Competitive edge will come from operational excellence through seamlessly distributed computations from cloud to edge.
The time series processing part of the AI-SPRINT framework could significantly accelerate the development of new backend modules analysing measurement data generated as current and historical images of damage to numerical measures reflecting the defined parameters and their evolution over time.
Potential societal or environmental impacts
Through the development of artificial intelligence models developed over the 3-year lifespan of the AI-SPRINT project, this use case will help reduce the environmental effects caused by malfunctioning wind power stations. It will also significantly contribute to energy efficiency and environmental sustainability. By fostering technology-based advances in maintenance and inspection, AI-SPRINT expects to contribute to the United Nation’s Sustainable Development Goal 9 on industry, innovation and infrastructure.