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Keywords: neural networksClose
Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T12A003, August 4–5, 2020
Paper No: POWER2020-16580
... traditional physics-based models. A long short-term memory (LSTM) model, a form of a recurrent neural network, was trained using operational datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. The LSTM turbine model was trained to predict shaft speed, outlet pressure...
Proc. ASME. POWER2007, ASME 2007 Power Conference, 279-282, July 17–19, 2007
Paper No: POWER2007-22022
... rotating machinery vibration fault diagnosis neural networks The fault diagnosis method based on artificial neural networks is summarized. An object-oriented paradigm is introduced to fault diagnosis for large scale rotating machinery, for example, turbine-generator. A fault diagnosis...
Proc. ASME. POWER2005, ASME 2005 Power Conference, 299-306, April 5–7, 2005
Paper No: PWR2005-50033
... calibration and neural techniques for status recognition. An application to a real plant shows the capabilities of the proposed methodology. Gas Turbine Inverse Problems Status Recognition Diagnosis Hybrid Algorithms Neural Networks Proceedings of PWR2005: ASME Power April 5-7, 2005, Chicago...