... 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...

... Engineering University of Maryland College of Engineering and Information Technology College Park, MD USA 20742 University of Maryland, Baltimore County *navid1@umd.edu Baltimore, MD 21250 ABSTRACT Keywords: offshore renewable energy systems, turbulent flow field, LSTM, convLSTM, deep learning, forecasting...

... Proceedings of the ASME 2021 Power Conference POWER2021 July 20-22, 2021, Virtual, Online POWER2021-64665 FORECASTING OF FOULING IN AIR PRE-HEATERS THROUGH DEEP LEARNING Ashit Gupta1, Vishal Jadhav1, Mukul Patil1, Anirudh Deodhar1, Venkataramana Runkana1 1TCS Research, Tata Consultancy Services...

... challenges for wind turbines nowadays. [1], although large-scale data in various stages of the product Here we present a CapsNet-based deep learning scheme for data- lifecycle, in terms of design, manufacturing, and service is driven fault diagnosis used in a digital twin of a wind turbine available...