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

A method for monitoring at least one 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:

• receiving, via an imaging analytics module of a controller, thermal imaging data of the rotor assembly, the thermal imaging data comprising a plurality of image frames;
• automatically identifying, via a first machine learning model of the imaging analytics module, a plurality of sections of the rotor assembly within the plurality of image frames until all sections of the rotor blade are identified;
• automatically identifying, via the imaging analytics module, one or more components of the rotor blade within the plurality of image frames;
• automatically identifying, via a second machine learning model of the imaging analytics module, a blade axis of the rotor blade using the one or more components, wherein automatically identifying, via the second machine learning model of the imaging analytics module, the blade axis of the rotor blade using the one or more components further comprises:
• detecting a position of the one or more components within the plurality of image frames;
• and
• automatically identifying, via the second machine learning model, the blade axis of the rotor blade based on the position of the one or more components;
• 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;
• and
• generating, via a visualization module of the controller, a complete image of the rotor assembly using the subset of image frames comprising all sections of the rotor blade.