Canadian software provider Clir Renewables has released its latest artificial intelligence (AI) feature able to learn how to identify anomalies in wind turbine component temperatures to detect failure at an earlier stage.
Clir’s technology can learn temperature behaviour in the context of real-world operational environment anomalies or trends that can be used to identify when a component is operating at higher than expected temperatures under certain conditions like increased loads.
Once identified, this information allows owners and operators to assess components for signs of degradation which if ignored could lead to catastrophic failure.
Clir says its AI can remove some of the unknowns around unexpected failures by creating actions for the owner or operator to investigate the turbine further.
In addition, Clir AI puts the data in context and takes into account a variety of factors including, but not limited to, service information, ambient temperature, rotor speed, ramping up and down, and seasonal variations.
Based on all of the information, the technology learns a model of the behaviour pattern for the turbine.
If the temperature varies outside the probabilistic range, the system creates events and actions on the system.
It reports multiple grades of severity, based on how much the trend deviates from the expected behaviour and learned failure models in the turbine.