Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

Martí de Castro-Cros, Manel Velasco and Cecilio Angulo
Additional contact information
Martí de Castro-Cros: Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Automatic Control Department, Universitat Politècnica de Catalunya, Campus Nord, Jordi Girona, 1-3, 08034 Barcelona, Spain
Manel Velasco: Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Automatic Control Department, Universitat Politècnica de Catalunya, Campus Nord, Jordi Girona, 1-3, 08034 Barcelona, Spain
Cecilio Angulo: Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Automatic Control Department, Universitat Politècnica de Catalunya, Campus Nord, Jordi Girona, 1-3, 08034 Barcelona, Spain

Energies, 2021, vol. 14, issue 24, 1-27

Abstract: Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.

Keywords: artificial intelligence; machine learning; soft sensor; condition assessment; gas turbine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/24/8468/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/24/8468/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:24:p:8468-:d:702954

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().