This paper develops a machine learning-based method to predict gas turbine performance for power generation. Two surrogate models based on high dimensional model …
During recent decades, artificial intelligence has been employed as a powerful tool for identification of complex industrial systems with nonlinear dynamics, such as gas turbines …
Condition monitoring, diagnostics, and prognostics are key factors in today's competitive industrial sector. Equipment digitalisation has increased the amount of available data …
B Li, YP Zhao, YB Chen - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
Intelligent data-driven fault diagnosis based on conventional machine learning techniques has been extensively studied in recent years. However, these methods often assumed that …
T Palmé, M Fast, M Thern - Applied energy, 2011 - Elsevier
Modern power plants are all strongly dependent on reliable and accurate sensor readings for monitoring and control, thus making sensors an important part of any plant. Failing …
S Zhong, S Fu, L Lin - Measurement, 2019 - Elsevier
A transfer learning method based on CNN and SVM is investigated for gas turbine fault diagnosis. The excellent classification ability of CNNs is attributed to their ability to learn rich …
In the paper, neural network (NN) models for gas turbine diagnostics are studied and developed. The analyses carried out are aimed at the selection of the most appropriate NN …
Fast prediction tools for turbine cooling performance have been demanded by industry for decades to support the iterative design process and the comprehensive response analysis …
K Hansson, S Yella, M Dougherty… - American Journal of …, 2016 - diva-portal.org
In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with …