Maintenance and Inspection
This use case was focused on the maintenance and inspection of the large turbines that are used for the (highly renewable) generation of electric power from wind. The inevitable damage to the turbines from the exposure to the elements can lead to expensive repairs and downtime; but traditionally, it has proven to be difficult to examine and evaluate the damage, which can consist of defects that are nearly invisible to the eye at a distance. Drones were employed to fly close to the turbines and send back imagery that can be efficiently processed across the computing continuum - and thereby permitting also effective preventive maintenance.
Maintenance and Inspection of wind turbines using AI models for detecting damage by collecting images from drones during their flight path and sending them to the edge-cloud channel for analysis. AI-SPRINT will accelerate inspection time by drastically reducing the time spent by operators to analyse damage or maintenance requirements and reducing human error. The solution will also enable lighter data pattern recognition routines and increase the reliability of windmill plants.
The underlying architecture maps into the reference AI-SPRINT architecture, including design and Run-Time tools specific to the Maintenance and Inspection Use Case. The deployable infrastructure consists of (1) Training nodes used to train the models (2) edge devices running VM nodes, which will typically run on a laptop and (3) a lightweight edge device on the drone itself.
All models will be trained on a public or private cloud, the inference will run on either an edge device or the cloud, also through FaaS, depending on the inference type .
At Run-Time level, SPACE4AI-R and Krake orchestrate the computation; MinIO provides local storage which can be synchronised with the cloud; IM and EC3 manage container deployment and elasticity, respectively.
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.
Drone operators thanks to the quick feedback on the photos taken during the inspection can redo some parts of the inspection without additional trip to the wind farm (instantaneous feedback).