FAQs About Turbit

Turbit is a Software as a Service Product that is based on a per module and per MW monitored pricing. Ask our experts to get a tailored offer.

Turbit significantly reduces operational risks and opex costs for your turbine fleet. Our AI detects small changes in the power curve and prevents major component failures, providing you with comprehensive insights to help prioritize maintenance and reduce downtime.

For the training of a module of Turbit we ideally need 24 months of data. If less data is present we can use transfer learning and start with one month of data. You can read here what kind of data you need for each module to work.

Turbit's AI monitoring system leverages artificial neural networks, machine learning, and advanced data engineering to provide real-time insights and predictive maintenance for wind turbines. Learn more about our technology here.

Turbit generates alarms with a false positive rate of less than 10%, allowing you to manage approximately 300 turbines in just 30 minutes per week. Typically, you can expect around 5 alarms per 100 turbines each week, depending on your fleet's status and quality.

No, Turbit is very easy to use and you should be able to understand everything at first use. On the EventCard every analysis is described and easy to understand. However, we give you an in-depth workshop at the beginning and continuous support in weekly or bi-weekly customer success workshops with our experts.

Turbit monitors key components and metrics of wind turbines, including power output and main components. Discover more details about our monitoring capabilities here.

Turbit monitoring works in real-time as well as fully automatic. Turbit has a failure mode prediction powered by AI.

Training Turbit's neural network takes less than a day. Once all data is available and checks are complete, we can have your entire fleet online within a week.

The answer highly depends on the data quantity and quality of your turbines data. Before we start a collaboration we do a preliminary data check to ensure maximum coverage. Turbit detects more than 35 different types of failures from unknown power curtailments or power curve problems to a clogged oil filter or a main bearing problem that needs a complete exchange.

No, Turbit is only based on software and works very well with SCADA data, there is no need for additional hardware. We can, however, use additional CMS or other sensor data like blade vibrations to get even better results and higher failure coverage.

Turbit uses Data Analytics and Language Models as well as manual checks to calculate a similarity score of signals — in other words a mapping probability. If these probabilities are off we perform manual checks and correct the signals. Often, signal mappings are changed over time, Turbit then detects an anomaly and a wrong mapping or frozen sensor signal can automatically be detected.

Predictive maintenance for wind turbines involves using advanced AI and machine learning algorithms to monitor the health and performance of turbines in real-time. This approach helps in predicting potential failures before they occur, allowing for timely maintenance and reducing downtime.

Predictive maintenance relies on various types of data, such as SCADA data, sensor readings, weather conditions, and historical performance data. This comprehensive data collection allows AI systems like Turbit to make accurate predictions and provide valuable insights for maintenance planning.

This is where individual turbine modeling becomes critical. Each machine has its own normal operating patterns based on its history, location, and component conditions. AI systems learn these individual baselines, which makes them much better at spotting true warning signs than fixed-threshold monitoring.

Good predictive maintenance systems learn from your feedback. When you mark an alarm as false or confirm it as valid, the system adjusts its sensitivity for similar situations. Over time, this reduces false positives while maintaining detection sensitivity for real issues.

Yes — most turbines built after 2005 have sufficient SCADA data for basic predictive maintenance. Newer turbines with more sensors provide richer data, but even basic parameters like temperatures, pressures, and power output can reveal developing problems and early warning signs.

Detection timeframes vary by component and failure mode. Bearing issues can often be spotted 3–6 months early. Some electrical problems may be visible 6–12 months before failure. The key is having enough historical data to understand normal patterns for each turbine — this is what lets early intervention prevent downtime.

Modern AI monitoring platforms are designed to be accessible to typical O&M teams. Instead of raw data analysis they provide interpreted results: root cause predictions and recommended actions. The goal is to augment your team's expertise, not replace it with complex data science.

Weather forecasting boosts energy output from healthy turbines. Predictive maintenance keeps turbines healthy and running. Both are necessary — weather predictions don't help if your gearbox fails during peak wind season.

Rising operational costs, aging renewable fleets, and insurance market hardening create economic pressure. AI monitoring offers one of the few scalable solutions for managing these increasing risks across the industry.

Many insurers now offer better terms for operators with proven predictive maintenance systems. The demonstrated risk reduction and proactive maintenance approach can lead to premium reductions and better coverage terms. Some operators see insurance savings that help offset their monitoring system costs.

In our experience with client data, operators typically see payback through prevented failures and reduced insurance premiums — particularly when they start with high-risk assets where early detection provides maximum value.

ROI varies by fleet size and current maintenance practices. Many operators see payback within 12–18 months. This comes through reduced unplanned downtime and optimized maintenance scheduling. The combination of avoided emergency repairs and better insurance terms often provides compelling economics.

Cloud-based AI platforms have cut setup costs dramatically. Many solutions now offer monthly fee structures per turbine, which makes them accessible for smaller operators — not just utility-scale portfolios.

Yes — most AI monitoring systems work with turbines from all major manufacturers. The key is having access to SCADA data, which is standard across the industry regardless of turbine brand. Some systems also work with existing condition monitoring equipment from various vendors.

Yes. Turbit is owned by its founders Michael Tegtmeier and Christian Fontius. Turbit is also backed with independent venture capital from Vinci Ventures.