System and method for monitoring wind turbine rotor blades using infrared imaging and machine learning

A system for monitoring a rotor assembly of a wind turbine, the rotor assembly comprising at least one of a rotatable hub and at least one rotor blade, the method comprising, the system comprising:

• a controller comprising:
• an imaging analytics module configured to perform a plurality of operations, the plurality of operations comprising:
• during a model-building time period, receiving input data comprising thermal imaging data of the rotor assembly, the thermal imaging data comprising a plurality of image frames;
• automatically identifying, via a machine learning model of the imaging analytics module, a plurality of sections of the rotor blade within the plurality of image frames until all sections of the rotor blade are identified;
• selecting, via a function of the imaging analytics module, a subset of image frames from the plurality of image frames, the subset of image frames comprising a minimum number of the plurality of image frames required to represent all sections of the rotor blade;
• automatically identifying, via the machine learning model, at least one anomaly and associated anomaly category information within the subset of image frames;
• generating output data comprising pixel information that corresponds to the at least one anomaly and the anomaly category information;
• and
• during a model-implementing time period, training the machine learning model over time using the input data and output data;
• and
• a visualization module configured to generate an image of the rotor assembly using the trained machine learning model.