Turbit AI can detect Rotor Bearing Failures months ahead

Detecting rotor bearing failures in wind turbines is crucial for ensuring their efficient and safe operation. Traditional methods of monitoring the temperature of main bearings can be time-consuming, expensive, and inefficient. However, with advancements in Artificial Intelligence (AI), it is now possible to detect potential failures with precision and efficiency.

In this article, we will discuss the AI monitoring system developed by Turbit, a company that specializes in developing AI-based monitoring systems for wind turbines. Michael, the CEO and founder of Turbit, explains how their AI monitoring system works and how it can detect main bearing failures with a precision of up to 0.5 degrees difference in temperature and months ahead of time.

The AI monitoring system developed by Turbit uses SCADA data of wind turbines, which includes the outside temperature, wind speed, rotor RPM speed, and other outside factors that affect the temperature of the main bearing. This data is used to train and learn the normal behavior of the temperature of the main bearing. The system can simulate how the temperature of the bearing should behave over time, even during extreme weather conditions.

As shown in the example, the AI monitoring system can detect abnormal behavior in the temperature of the main bearing. The system compares the actual temperature data of the main bearing with the simulated data, and if there is a significant difference, it sends an alarm to the operators. The alarm is escalated if the problem persists and, in some cases, triggers a service event.

In the example provided by Michael, the AI monitoring system detected a problem with the main bearing in January. The temperature of the main bearing had increased over time, indicating a potential problem. The system sent an alarm, which was escalated when the problem persisted. A service event was triggered, and the problem was eventually solved by repairing the greasing mechanism.

The AI monitoring system developed by Turbit can detect potential failures with a precision of up to 0.5 degrees difference in temperature and months ahead of time. This provides operators with ample time to plan and schedule maintenance activities, minimizing downtime and preventing worst-case scenarios.

In conclusion, AI-based monitoring systems like the one developed by Turbit can provide a more efficient and cost-effective solution for detecting potential failures in wind turbines. With the ability to detect potential problems with a high degree of accuracy and well in advance, operators can minimize downtime and prevent costly repairs.