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Keywords: convolutional neural networkClose
Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T22A019, June 7–11, 2021
Paper No: GT2021-60247
... Proceedings of ASME Turbo Expo 2021 Turbomachinery Technical Conference and Exposition GT2021 June 7-11, 2021, Virtual, Online GT2021-60247 RESEARCH ON FAULT DIAGNOSIS OF STEAM TURBINE ROTOR UNBALANCE AND PARALLEL MISALIGNMENT BASED ON NUMERICAL SIMULATION AND CONVOLUTIONAL NEURAL NETWORK Chongyu...
Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T22A018, June 7–11, 2021
Paper No: GT2021-60049
... and predictive maintenance which can help the improvement of power system. In this study, Keywords: Multi-parameter prediction; Deep learning; deep-learning models including recurrent neural network (RNN) and convolutional neural network (CNN) for multi-parameter Machine learning; Recurrent neural network...
Proc. ASME. GT2021, Volume 9A: Structures and Dynamics — Aerodynamics Excitation and Damping; Bearing and Seal Dynamics; Emerging Methods in Design and Engineering, V09AT23A017, June 7–11, 2021
Paper No: GT2021-60075
...Abstract Abstract In this paper, a novel model is presented for reconstructing unsteady periodic fields of velocity vector and pressure scalar over an oscillating foil. This data-driven method based on convolutional neural network can be utilized to accomplish two objections: fields...
Proc. ASME. GT2020, Volume 10B: Structures and Dynamics, V10BT27A019, September 21–25, 2020
Paper No: GT2020-15097
... to employ efficient techniques to consider all the types of damage. Deep neural networks were seen to exhibit the ability to address similar complex problems. The research question in this work is ‘Can data fusion improve damage classification using the convolutional neural network?’ The specific aims...