Wind Energy: Reaching New Heights in Diagnostics
Russia’s wind energy sector continues to grow impressively, inevitably leading to an expanding fleet of wind turbines that require regular and high-quality maintenance. The key challenge of September 2025 was the transition from scheduled maintenance to predictive maintenance — when repairs are carried out based on the actual condition of equipment rather than fixed intervals. This is where the synergy of drones and AI reveals its full potential.
Modern multirotor drones equipped with ultra-high-resolution cameras, thermal imagers, and LiDAR systems perform full-scale inspections of turbine blades at heights exceeding 150 meters. Previously, such operations required halting the turbine, deploying industrial climbers, and using lifting platforms — a process that took days and carried significant human risks. Today, a drone completes a detailed scan of a single blade in just fifteen minutes without stopping electricity generation.
However, the true revolution lies not in data collection, but in its analysis. Instead of a human expert spending hours reviewing images for microcracks, composite delamination, or erosion marks from sand particles, artificial intelligence now performs this task. Trained on millions of images, AI algorithms can detect anomalies smaller than a millimeter with accuracy exceeding human capability. But their greatest advantage lies in predictive analytics.
AI not only identifies defects but also analyzes their dynamics by comparing data from previous inspections, building a model of how the damage will develop. The system calculates the remaining service life of the component and forecasts — with week-level precision — when a defect will reach critical thresholds and require intervention. This enables operators to plan maintenance campaigns optimally, pre-order materials, and allocate specialists, minimizing downtime and financial losses.
A notable example comes from a new wind farm in the Rostov region, where under a contract with Rosatom, all maintenance is performed using drone-based technologies. In September, a full monitoring system was deployed: drones autonomously follow set routes, upload data to the cloud, where AI processes it and automatically generates maintenance requests in the company’s ERP system — including exact defect coordinates and material requirements.
Mining Industry: The Digital Twin of the Quarry
The mining industry faces different challenges but applies similar technological responses. Quarries are dynamic environments where thousands of cubic meters of rock are moved daily, creating risks of slope collapse and requiring continuous monitoring of extraction volumes and equipment condition. Traditional surveying methods cannot keep up with the pace of change and pose risks to personnel.
By September 2025, creating digital twins of quarries — updated in near real time — had become a major trend. These twins are built from aerial data collected by fixed-wing UAVs performing daily LiDAR scans and multispectral imaging missions.
The resulting point clouds and high-precision orthophotos are processed by computer vision algorithms that automatically calculate overburden volumes, track slope movements, and verify that slope angles comply with design parameters. Any deviation is immediately flagged, allowing engineers to prevent landslides or collapses before they occur.
A strong example of successful implementation is the slope stabilization project at the Kovdor Mining and Processing Plant completed in September. Before construction began, drones conducted high-precision mapping to create a digital quarry model. Based on this model, engineers used specialized software to calculate slope stability and design optimal reinforcement structures. All work was performed without stopping production, with drones continuing real-time monitoring to ensure the safety of workers and equipment.
Another growing application is environmental monitoring. After the well-known Sibay quarry flooding incident, constant drone patrols were introduced over similar sites. These UAVs not only track water levels but also use hyperspectral cameras to analyze water and air composition — identifying potential contamination long before it reaches critical concentrations or spreads beyond the site.
Technology Synergy: When One Plus One Equals Three
The use of drones or AI alone already delivers benefits, but true breakthroughs come from their deep integration. Drones act as flying sensors, collecting vast datasets (big data), while AI serves as the analytical “brain” that transforms this raw input into actionable insights and operational decisions.
In wind power, the process looks like this: the drone captures imagery, AI analyzes it, diagnoses defects, and then integrates with the company’s management system to generate a repair order for a specific blade on a specific turbine — including crane and part requirements.
In mining, the system not only constructs 3D models from aerial data but automatically adjusts haul-truck routes to optimize logistics and reduce fuel consumption. AI also predicts tire wear based on road surface analysis, ensuring timely replacements and preventing unscheduled downtime.
Challenges and Barriers to Technological Transition
Despite its clear advantages, the mass adoption of drone–AI systems faces several challenges.
The first is regulatory — flights over critical infrastructure such as wind farms or mining sites require special permits and approvals, which often lag behind technological progress.
The second is human resources — new professionals are needed: UAV operators capable not only of piloting but also managing complex sensor payloads, as well as data specialists who can interpret AI outputs and make informed engineering decisions.
The third is infrastructure — processing massive datasets requires powerful computing resources, typically cloud-based, which may be difficult to access in remote regions with poor internet connectivity. Edge computing, where preliminary data processing occurs directly on the drone or local servers, partially mitigates this issue.
The Future Is Already Here: Development Prospects
September 2025 clearly outlined the direction for the near future.
First, full autonomy — drone manufacturers are actively testing systems that allow UAVs to self-dock on charging stations located at industrial sites and perform scheduled flights autonomously, adjusting routes for weather conditions.
Second, swarm technology — coordinated drone fleets working together as a single system. This approach will allow entire wind farms or quarries to be inspected simultaneously from multiple angles, dramatically accelerating the process.
Third, deeper AI integration — algorithms will not only analyze data but also make real-time decisions, such as halting equipment upon detecting early signs of a landslide or automatically dispatching maintenance crews when critical turbine defects are identified.
Conclusion
September 2025 confirmed that accelerated digitalization through drone and AI adoption has moved beyond pilot projects into mature industrial deployment. The wind energy and mining sectors have become proving grounds for these technologies, demonstrating significant improvements in safety, cost reduction, and operational efficiency. The synergy of flying robots and intelligent algorithms is creating a new standard in asset management — one driven by data and predictive insights rather than schedules or intuition.
This trend is irreversible and, in the coming years, will extend across all industrial sectors, defining the face of the new industrial era.