Scalable system and method for forecasting wind turbine failure with varying lead time windows

Abstract

An example method utilizing different pipelines of a prediction system, comprises receiving failure data, and asset data from SCADA system(s), receiving and dividing historical sensor data from sensors of components of wind turbines into different classes of different lead times, training a set of models to predict faults for each component using the historical sensor data and lead times with a deep neural network, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.