The progress of machine learning (ML) in the past years has opened up new opportunities
to the field of gas turbine (GT) modelling. However, successful implementation of ML …

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 …

Intelligent data-driven fault diagnosis based on conventional machine learning techniques
has been extensively studied in recent years. However, these methods often assumed that …

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 …

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 …

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 …