Wind Turbine Main Bearing Failure Prediction using a Hybrid Neural Network
The TU Delft Wind Energy Institute
Jan 2021 - Mar 2022 (1 year 3 months)
- Create a hybrid neural network and test it
In this work, it is proposed a hybrid neural network for main bearing failure prognosis. This network consists of a two-dimensional convolutional neural network (to extract spatial-temporal characteristics from the data) sequentially connected with a long short-term memory network (to learn sequence patterns) to predict the slow-speed shaft temperature (the closest temperature to the main bearing). The mean square error between its real measurement and its prediction gives a failure indicator. When it is greater than a defined threshold, then an alarm is triggered and gives the maintenance staff time to check the component.