Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system

被引:41
|
作者
Hanachi, Houman [1 ,2 ]
Liu, Jie [1 ,3 ]
Mechefske, Christopher [2 ]
机构
[1] Chongqing Technol & Business Univ, Natl Res Base Intelligent Mfg Serv, Chongqing 400067, Peoples R China
[2] Queens Univ, Dept Mech & Mat Engn, Kingston, ON K7L 3N6, Canada
[3] Carleton Univ, Dept Mech & Aerosp Engn, Ottawa, ON K1S 5B6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
ANFIS; Fault detection; Multi-mode diagnosis; Measurement noise; Real-time diagnosis; NETWORKS;
D O I
10.1016/j.cja.2017.11.017
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Gas Turbine Engines (GTEs) are vastly used for generation of mechanical power in a wide range of applications from airplane propulsion systems to stationary power plants. The gas-path components of a GTE are exposed to harsh operating and ambient conditions, leading to several degradation mechanisms. Because GTE components are mostly inaccessible for direct measurements and their degradation levels must be inferred from the measurements of accessible parameters, it is a challenge to acquire reliable information on the degradation conditions of the parts in different fault modes. In this work, a data-driven fault detection and degradation estimation scheme is developed for GTE diagnostics based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). To verify the performance and accuracy of the developed diagnostic framework on GTE data, an ensemble of measurable gas path parameters has been generated by a high-fidelity GTE model under (a) diverse ambient conditions and control settings, (b) every possible combination of degradation symptoms, and (c) a broad range of signal to noise ratios. The results prove the competency of the developed framework in fault diagnostics and reveal the sensitivity of diagnostic results to measurement noise for different degradation symptoms. (C) 2017 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.
引用
收藏
页码:1 / 9
页数:9
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