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.
Highlights
Assessment of Gas Turbines—AIn today’s competitive industry, increasing the reliability, availability, and safety of equipment, while reducing the operational and maintenance expenses are key factors of profitability and competitiveness
The main goal of this paper is to review and discuss the new techniques that have emerged the last decade in the area of machine learning for condition monitoring, diagnosis, and prognosis of gas turbines
Hybrid methodologies that included model-based algorithms were excluded; Documents that were related to the simulation of gas turbines, as well as their design were excluded; Publications devoted to enhancing the standard control systems of gas turbines using machine-learning techniques were not accepted
Summary
Assessment of Gas Turbines—AIn today’s competitive industry, increasing the reliability, availability, and safety of equipment, while reducing the operational and maintenance expenses are key factors of profitability and competitiveness. The big amount of data captured by industrial systems contains information about components, events, and alarms related to industrial processes All these data can provide significant knowledge and information about system processes and their dynamics. Data analytics using machine-learning techniques is able to treat large amounts of data and acquire online information about the machine’s status These procedures are mostly used to obtain information from multidimensional time series to identify hidden patterns and managerial results for strategic decision-making [4]. Gas turbines are a type of internal combustion engine that converts the chemical energy of fuel into electrical power. They are mainly made of three components: compressor, combustor, and power turbine [8]. The combustor must be well designed in order to provide a complete combustion process and avoid malfunctioning
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