An Uncertainty-Aware Health Monitoring Model for Wind Turbine Drivetrains Based On Bayesian Neural Network

Abstract

Vibration-based fault diagnostics in rotating machinery when combined with deep learning approaches have yielded promising results. Nevertheless, standard deep learning methods can be unreliable when faced with new data from previously unseen faults or unusual operating conditions. To overcome this challenge, a Bayesian convolutional neural network is explored in this study to diagnose faults in wind turbine gearboxes. This Bayesian statistics framework provides accurate results with low uncertainty by evaluating test data with the same training data distribution. When presented with data that it has not seen before, the network signals its uncertainty and recommends human intervention. This helps to reduce the likelihood of incorrect diagnoses resulting from wrong overconfident classifications. The study compares the performance of Bayesian and standard neural networks using a simulation-based database of acceleration signals. These signals are represented as 2D cyclic spectral coherence maps generated from a multi-body dynamic model of a five-Megawatt wind turbine. By incorporating uncertainty into the prediction results, this approach has the potential to significantly reduce false positives and improve maintenance operations’ efficiency and effectiveness.