How drone-based predictive maintenance is transforming offshore wind O&M in 2025

Offshore wind farms like Ørsted’s West of Duddon Sands are entering a new era of operational optimization, where efficiency is no longer defined by capacity alone, but by how intelligently infrastructure is maintained. As the global offshore wind fleet matures—particularly projects commissioned before 2015—operators are facing a complex set of challenges. These include escalating operations and maintenance (O&M) costs, unpredictable component wear from harsh marine conditions, technician safety risks, and increasing regulatory pressure to decarbonize ancillary operations such as crew transfer vessels (CTVs) and diesel-powered service logistics.

In response, the industry is undergoing a digital transformation in its approach to asset management. Predictive maintenance—once confined to theory and pilot projects—has now become a cornerstone of long-term O&M planning, particularly at sites where margins are increasingly determined by uptime efficiency rather than new capacity additions. In 2025, drone-based inspection technology, when integrated with AI-powered analytics platforms, is redefining how offshore wind assets are monitored, serviced, and optimized.

Ørsted, one of the most active offshore wind developers globally, has integrated drone-enabled predictive maintenance at several sites, with West of Duddon Sands serving as a case study of legacy asset modernization. Rather than deploying teams of rope-access technicians or relying on time-based inspection intervals, operators now use autonomous drones to conduct high-resolution, multispectral scans of turbine blades, nacelles, and towers. These data streams are processed through cloud-hosted AI models capable of flagging micro-fractures, surface pitting, lightning strike damage, and structural anomalies long before they manifest as performance losses or safety hazards.

This shift allows operators to move from reactive to proactive maintenance. Issues are addressed based on data-driven urgency rather than routine schedules, reducing both downtime and O&M costs. Predictive models also support component life extension strategies by helping operators understand when a blade or gearbox might fail—and under what conditions—enabling informed decisions about repairs versus replacements. For a project like West of Duddon Sands, now more than a decade into its operational life, such intelligence is invaluable in extracting full lifecycle value from first-generation turbine technology.

Importantly, drone-based predictive maintenance is also helping to decarbonize the maintenance chain. Traditional inspection routines involve crewed vessels making hundreds of trips per year, often burning diesel fuel and requiring favorable sea states. Drones, by contrast, can be deployed from shore bases or jack-up vessels already stationed near wind farms, significantly reducing emissions, costs, and scheduling constraints.

In 2025, the adoption of these technologies is no longer limited to experimental pilot programs. Institutional investors, ESG auditors, and even insurers now view predictive maintenance maturity as a marker of asset resilience. Wind farms that demonstrate high availability through advanced diagnostics often secure more favorable financial terms and insurance premiums. For operators, it’s not just about maintenance—it’s about asset reputation, regulatory alignment, and long-term profitability in an increasingly competitive market.

As drone hardware becomes more autonomous and analytics engines more accurate, predictive maintenance is expected to evolve further—from rule-based decision-making to fully prescriptive O&M, where systems not only identify what’s wrong but also suggest the most efficient course of action. In this context, projects like West of Duddon Sands are more than just power generators; they are testbeds for the next frontier of intelligent infrastructure in offshore wind.

Why are offshore wind operators turning to drone-based maintenance strategies in 2025?

Traditionally, turbine inspections at sea required manned vessel access and rope teams, making the process time-consuming, costly, and potentially hazardous. As these offshore wind assets age, especially those commissioned before 2015, the frequency and complexity of inspections increase. In 2025, predictive maintenance using drone inspections and AI analysis is being adopted to enhance safety and lower downtime. Industry observers note that the approach helps streamline fault detection, reduce the need for physical technician access, and prevent major mechanical failures before they escalate.

How is Ørsted using AI and drone inspection to maintain West of Duddon Sands Offshore Windfarm?

At the West of Duddon Sands windfarm—operational since 2014—Ørsted has integrated drone-based inspections into its maintenance routines. Drones equipped with high-resolution imaging and thermal sensors are deployed to inspect blades, nacelles, and towers. The collected data is processed using cloud-based platforms such as Microsoft Azure and Cognite Data Fusion. While Ørsted has not attributed specific performance metrics to drone use at this site, its broader offshore portfolio has seen improvements in inspection speed, safety, and response times due to similar technologies. The shift has reduced technician exposure to confined turbine spaces and accelerated damage detection across the site.

What technologies power predictive maintenance in offshore wind: drones, sensors, or software?

Predictive maintenance in 2025 relies on a technology stack that blends hardware and software. Drones serve as the primary data collection tool, capturing visual, infrared, and LiDAR imagery. This data is fed into AI models hosted on cloud platforms that use machine learning to identify signs of structural fatigue, lightning strikes, or surface degradation. Companies like Ørsted and Equinor are integrating digital twins of turbine components, allowing them to simulate stress, failure rates, and performance under varying load conditions. The result is a more responsive, real-time approach to asset maintenance.

How does predictive maintenance affect turbine availability and capacity factors at aging wind farms?

Although Ørsted does not publish site-specific availability data tied to predictive maintenance, industry benchmarks suggest that legacy assets like West of Duddon Sands continue to maintain availability levels above 96%. Capacity factors at the site have historically ranged from 42% to 44%, aligning with averages for Irish Sea projects commissioned in the early 2010s. Predictive maintenance tools are not solely responsible for sustaining this performance, but they likely play a supportive role in minimizing unplanned outages and improving the timing of component replacements.

What are the cost, ESG, and safety benefits of AI-powered inspections in offshore energy?

The operational benefits of AI-enabled drone inspections are well documented. Replacing vessel-based visual inspections with drones reduces logistics costs, fuel consumption, and weather-related delays. Safety improves as human exposure to high-risk offshore tasks decreases. From an ESG standpoint, the strategy supports lower-carbon operations by reducing vessel trips and enabling targeted interventions rather than routine maintenance. For investors in infrastructure funds, these efficiency gains can improve internal rates of return without compromising sustainability goals.

Which offshore wind operators are leading in predictive analytics and AI-based O&M workflows?

Ørsted has been among the first movers in this space, adopting drone inspections in collaboration with partners such as SkySpecs. Other operators, including Equinor and RWE, have invested in condition monitoring and AI analytics to support their offshore portfolios. Siemens Gamesa has integrated sensor data and computer vision models into its service offerings, while RES has acquired Sulzer Schmid to offer drone-as-a-service solutions for blade inspections. These developments point to a sector-wide shift toward automated diagnostics, with cloud-native platforms driving real-time decision-making across multiple gigawatt-scale assets.

Is drone-based predictive maintenance scalable to gigawatt-scale offshore wind farms?

Scaling predictive maintenance to very large sites like Hornsea 2, Dogger Bank, or East Anglia Hub presents logistical and data management challenges. Operators are currently trialing centralized drone fleets, AI coordination dashboards, and remote-control launch stations to handle the scale of inspections required. While not yet the default method across all gigawatt-class projects, predictive drone maintenance is rapidly gaining traction and could become standard within the next two to three years as regulators adapt and fleet management tools mature.

What does the future of offshore wind O&M look like beyond 2025?

The next generation of offshore wind O&M will likely combine autonomous drones, underwater robotics, and AI-driven prescriptive analytics. Companies like Aerones and Skyspecs are developing drone platforms that go beyond inspection to perform cleaning, lubrication, and minor repairs. Meanwhile, digital twins will evolve from diagnostic tools into predictive engines that model turbine lifespan and recommend asset-wide interventions. For legacy wind farms such as West of Duddon Sands, these technologies could play a pivotal role in extending operational viability well into the 2030s.