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Open Access

REVIEW

A Review on Fault Diagnosis Methods of Gas Turbine

Tao Zhang1,*, Hailun Wang1, Tianyue Wang1, Tian Tian2
1 Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China
2 Systems Engineering Research Institute, China State Shipbuilding Corporation Limited, Beijing, 100094, China
* Corresponding Author: Tao Zhang. Email: email
(This article belongs to the Special Issue: Signal Processing for Fault Diagnosis)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072696

Received 02 September 2025; Accepted 30 October 2025; Published online 05 December 2025

Abstract

The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation, mechanical wear, and airflow disturbances during prolonged operation. These conditions can lead to a series of issues, including mechanical faults, air path malfunctions, and combustion irregularities. Traditional model-based approaches face inherent limitations due to their inability to handle nonlinear problems, natural factors, measurement uncertainties, fault coupling, and implementation challenges. The development of artificial intelligence algorithms has provided an effective solution to these issues, sparking extensive research into data-driven fault diagnosis methodologies. The review mechanism involved searching IEEE Xplore, ScienceDirect, and Web of Science for peer-reviewed articles published between 2019 and 2025, focusing on multi-fault diagnosis techniques. A total of 220 papers were identified, with 123 meeting the inclusion criteria. This paper provides a comprehensive review of diagnostic methodologies, detailing their operational principles and distinctive features. It analyzes current research hotspots and challenges while forecasting future trends. The study systematically evaluates the strengths and limitations of various fault diagnosis techniques, revealing their practical applicability and constraints through comparative analysis. Furthermore, this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future.

Keywords

Fault diagnosis; machine learning; gas turbine; artificial intelligence; deep learning

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