A Physics-Based Modeling Approach for Performance Monitoring in Gas Turbine Engines

被引:77
作者
Hanachi, Houman [1 ]
Liu, Jie [1 ]
Banerjee, Avisekh [2 ]
Chen, Ying [2 ]
Koul, Ashok [2 ]
机构
[1] Carleton Univ, Dept Mech & Aerosp Engn, Ottawa, ON K1S 5B6, Canada
[2] Life Predict Technol Inc, Ottawa, ON K1J 9J1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Health monitoring; performance deterioration; physics-based modeling; variable operating condition; DIAGNOSIS; ONLINE; PROGNOSIS;
D O I
10.1109/TR.2014.2368872
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Performance deterioration monitoring is an essential part of the prognostics and health management (PHM) of gas turbine engines (GTEs). This paper proposes a physics-based modeling approach for performance deterioration monitoring with two model-based performance indicators, heat loss index and power deficit index, for GTE PHM applications. A comprehensive nonlinear thermodynamic model for a single shaft GTE is developed to establish the relation between the operating conditions and the cycle parameters. The model, once properly calibrated, is able to predict the GTE cycle parameters in a healthy condition as the baseline, while in reality, the measured parameters gradually deviate from the baseline, which reflects the performance deterioration of the GTE. To represent the degradation level, the heat loss index is defined as the normalized measure of the thermal power that is being wasted in the GTE compared to the healthy condition. Similarly, the power deficit index is defined as the deficiency ratio of the GTE output power due to the performance deterioration. The effectiveness of the performance indicators in monitoring performance deterioration and their robustness to the variations of the operating conditions are examined by using three years of typical operating data of an industrial GTE. The results clearly reveal the trends of both the short term recoverable deterioration due to fouling effects in the compressor, and the long term non-recoverable deterioration caused by structural degradation. The technique is especially advantageous for prognostic applications where there is no access to internal cycle parameters of a GTE, and only the operating data are available, hence no additional sensors are required.
引用
收藏
页码:197 / 205
页数:9
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