Research Article Open Access

Machine Learning Approaches for the Prediction of Gas Turbine Transients

Arnaud Nguembang Fadja1, Giuseppe Cota2, Francesco Bertasi2, Fabrizio Riguzzi3, Enzo Losi1, Lucrezia Manservigi1, Mauro Venturini1 and Giovanni Bechini4
  • 1 Department of Engineering, University of Ferrara,Via Saragat 1, 44122, Ferrara, Italy
  • 2 Exalens Srl, Via Aldo Moro 6, 45100, Rovigo, Italy
  • 3 Department of Mathematics and Computer Science, University of Ferrara, Via Machiavelli 30, 44121, Ferrara, Italy
  • 4 Siemens Energy, Srl, Via Vipiteno, 4, 20128, Milan, Italy


Gas Turbine (GT) emergency shutdowns can lead to energy production interruption and may also reduce the lifespan of a turbine. In order to remain competitive in the market, it is necessary to improve the reliability and availability of GTs by developing predictive maintenance systems that are able to predict future conditions of GTs within a certain time. Predicting such situations not only helps to take corrective measures to avoid service unavailability but also eases the process of maintenance and considerably reduces maintenance costs. Huge amounts of sensor data are collected from (GTs) making monitoring impossible for human operators even with the help of computers. Machine learning techniques could provide support for handling large amounts of sensor data and building decision models for predicting GT future conditions. The paper presents an application of machine learning based on decision trees and k-nearest neighbors for predicting the rotational speed of gas turbines. The aim is to distinguish steady states (e.g., GT operation at normal conditions) from transients (e.g., GT trip or shutdown). The different steps of a machine learning pipeline, starting from data extraction to model testing are implemented and analyzed. Experiments are performed by applying decision trees, extremely randomized trees, and k-nearest neighbors to sensor data collected from GTs located in different countries. The trained models were able to predict steady state and transient with more than 93% accuracy. This research advances predictive maintenance methods and suggests exploring advanced machine learning algorithms, real-time data integration, and explainable AI techniques to enhance gas turbine behavior understanding and develop more adaptable maintenance systems for industrial applications.

Journal of Computer Science
Volume 20 No. 5, 2024, 495-510


Submitted On: 7 November 2022 Published On: 28 February 2024

How to Cite: Fadja, A. N., Cota, G., Bertasi, F., Riguzzi, F., Losi, E., Manservigi, L., Venturini, M. & Bechini, G. (2024). Machine Learning Approaches for the Prediction of Gas Turbine Transients. Journal of Computer Science, 20(5), 495-510.

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  • Gas Turbines
  • Multi-Variate Time Series
  • Decision Tree
  • Extra Trees
  • K-Nearest Neighbour
  • Transient Prediction