首页|Intelligent fault diagnosis methods toward gas turbine:A review

Intelligent fault diagnosis methods toward gas turbine:A review

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Fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs)to improve reliability and reduce costs.Various diagno-sis methods developed by modeling engine systems or certain components implement faults detec-tion and diagnosis based on the measurement of systemic parameters deviations.However,these conventional model-based methods are hindered by limitations of inability to handle the nonlinear nature,measurement uncertainty,fault coupling and other implementing problems.Recently,the development of artificial intelligence algorithms has provided an effective solution to the above problems,triggering broad researches for data-driven fault diagnosis methods with better accuracy,dynamic performance,and universality.This paper presents a systematic review of recently pro-posed intelligent fault diagnosis methods for GT engines,according to the classification of shallow learning methods,deep learning methods and hybrid intelligent methods.Moreover,the principle of typical algorithms,the evolution of enhanced methods,and the assessment of pros and cons are summarized to conclude the present status and look forward to the future in the field of GT fault diagnosis.Possible directions for development in method validation,information fusion,and inter-pretability of intelligent diagnosis methods are concluded in the end to provide insightful concepts for scholars in related fields.

Fault diagnosisHealth managementGas turbineArtificial intelligenceIntelligent diagnosis method

Xiaofeng LIU、Yingjie CHEN、Liuqi XIONG、Jianhua WANG、Chenshuang LUO、Liming ZHANG、Kehuan WANG

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School of Transportation Science and Engineering,Beihang University,Beijing 100191,China

Systems Engineering Research Institute,China State Shipbuilding Corporation,Beijing 100094,China

国家自然科学基金国家自然科学基金国家自然科学基金key projects of Aero Engine and Gas Turbine Basic Science Centerkey projects of Aero Engine and Gas Turbine Basic Science Center

618909216189092352372371P2022-B-V-001-001P2022-B-V-002-001

2024

10.1016/j.cja.2023.09.024

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(4)
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