... Abstract Gas turbine systems are widely used in the power industry because they provide continuous and reliable power to the electrical grid. One of the main concerns for implementing gas turbine systems is the maintenance costs. Therefore, predictive maintenance methods driven by Deep Learning...

... the conventional Prediction-Correction (PC) method and modern Physics-informed Neural Network (PINN). It was shown that the physics-informed deep learning method provides good computational efficiency in resolving the steep pressure gradient in the clearance with good accuracy. The results showed that the leakage...

...-driven techniques can complement existing physics-based approaches for complex problems such as wind farm wake modeling. In this paper, a deep learning model is developed to predict the local short-term wind characteristics. A data pre-processing pipeline that includes data cleaning and normalizing steps...

... APH and the complex thermo-chemical phenomena, fouling is quite unpredictable. We present a deep learning based model for forecasting the gas differential pressure across the APH using the Long Short Term Memory (LSTM) networks. The model is trained and tested with data generated by a plant model...

... turbines nowadays. Here we present a CapsNet-based deep learning scheme for data-driven fault diagnosis used in a digital twin of a wind turbine gearbox. The CapsNet model can extract the multi-dimensional features and rich spatial information from the gearbox monitoring data by an artificial neural...