Issue Models in FFM

Understanding how WindESCo uses physical models, machine learning, and AI to identify issues

Many of our customers have asked how WindESCo applies advanced analytics approaches appropriately - check out our general approach below to understand a little more about the step-by-step process

1. First, we take physical principles and expert knowledge of wind turbines to drive analysis and results in technically sound conclusions
   1. This includes mechanical, aerodynamic, control system and field expertise drive specific anomaly identification 
   2. Servo-mechanical expertise informs analysis of expected and actual behavior
   3. Essentially: Is the turbine controller doing what it should be doing, when it should be doing it?
2. We then use innovative signal processing and machine learning applications to advance subject matter expert (SME) capabilities
3. Machine learning and AI enhance models of anomaly detection and normal behavior when it is appropriate
   1. These approaches enable rapid automation and scaling of SME expertise
   2. Examples include: Clustering, Identification of derating, Changes across turbines or time

It is important that the approach is not simply a data in / data out approach, but instead uses physical understanding of behavior to drive proper input/output relationships.  For example, we know that correlation does not equal causation and our team's expertise enables AI/ML to find actionable causation.