Fluid Mechanics and Flight Mechanics

Design and Simulation Validation of an Integrated On-board Aircraft Engine Diagnostic Architecture

  • ZHANG Shugang ,
  • GUO Yingqing ,
  • FENG Jianpeng
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  • School of Power and Energy, Northwestern Polytechnical University, Xi'an 710072, China

Received date: 2013-04-09

  Revised date: 2013-05-13

  Online published: 2013-05-20

Supported by

National Level Project

Abstract

Continuing advances in avionics are enabling the migration of portions of the conventional ground-based functionality on-board. The availability of real-time data can monitor the engine performance deterioration on-board, decrease fault detection and isolation latency and increase detection probability of intermittent engine faults. This paper presents a design of an on-board diagnostic architecture for aircraft engine gas path fault detection and isolation, health trend monitoring and parameter estimation. A hardware simulation platform which runs in real time is developed based on xPC Target to evaluate the performance of the structure. Simulation results show that estimation errors by the on-board adaptive model of the structure are below 0.5% for the engine health, including both measured parameters and unmeasured parameters. The gas path fault diagnostic system can detect and isolate all kinds of gas path faults including intermittent faults earlier with real-time data.

Cite this article

ZHANG Shugang , GUO Yingqing , FENG Jianpeng . Design and Simulation Validation of an Integrated On-board Aircraft Engine Diagnostic Architecture[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014 , 35(2) : 381 -390 . DOI: 10.7527/S1000-6893.2013.0255

References

[1] Litt J S, Simon D L, Garg S, et al. A survey of intelligent control and health management technologies for aircraft propulsion systems, NASA/TM-2005-213622, ARL-TR-3413[R]. Cleveland, OH: NASA, 2005.

[2] Jaw L C. Recent advancements in aircraft engine health management (EHM) technologies and recommendations for the next step, ASME Paper, GT-2005-68625[R]. Reno: ASME, 2005.

[3] Wei X K, Feng Y, Liu F, et al. Development strategy and key prognostics health management technologies for military aero-engine in China[J]. Journal of Aerospace Power, 2011, 26(9): 2107-2115. (in Chinese) 尉询楷, 冯悦, 刘芳, 等. 军用航空发动机PHM发展策略及关键技术[J]. 航空动力学报, 2011, 26(9): 2107-2115.

[4] Jiang C H, Sun Z Y, Wang X. Critical technologies for aero-engine prognostics and health management systems development[J]. Journal of Aerospace Power, 2009, 24(11): 2589-2594.(in Chinese) 姜彩虹, 孙志岩, 王曦. 航空发动机预测健康管理系统设计的关键技术[J]. 航空动力学报, 2009, 24(11): 2589-2594.

[5] Kobayashi T, Simon D L. Integration of on-line and off-line diagnostic algorithms for aircraft engine health management[J]. Journal of Engineering for Gas Turbines and Power, 2007, 129(4): 986-993.

[6] Simon D L, Garg S. Optimal tuner selection for Kalman filter-based aircraft engine performance estimation[J]. Journal of Engineering for Gas Turbines and Power, 2010, 132(3): 031601.1-031601.10.

[7] Simon D L. An integrated architecture for on-board aircraft engine performance trend monitoring and gas path fault diagnostics, NASA/TM-2010-216358[R]. Cleveland, OH: NASA, 2010.

[8] Armstrong J B, Simon D L. Implementation of an integrated on-board aircraft engine diagnostic architecture, NASA/TM-2012-217279[R]. Cleveland, OH: NASA, 2012.

[9] Yuan C F, Yao H, Yang G. On-board real-time adaptive model of aero-engine[J]. Acta Aeronautica et Astronautica Sinica, 2006, 27(4): 561-564.(in Chinese) 袁春飞, 姚华, 杨刚. 航空发动机机载实时自适应模型研究[J]. 航空学报, 2006, 27(4): 561-564.

[10] Zhang H B, Chen T H, Sun J G, et al. Design and simulation of a new novel engine adaptive model[J]. Journal of Propulsion Technology, 2011, 32(4): 557-563.(in Chinese) 张海波, 陈霆昊, 孙健国, 等. 一种新的航空发动机自适应模型设计与仿真[J]. 推进技术, 2011, 32(4): 557-563.

[11] Lu J, Guo Y Q, Zhang S G. Aeroengine on-board adaptive model based on improved hybrid Kalman filter[J].Journal of Aerospace Power, 2011, 26(11): 2593-2600.(in Chinese) 陆军, 郭迎清, 张书刚. 基于改进混合卡尔曼滤波器的航空发动机机载自适应模型[J]. 航空动力学报, 2011, 26(11): 2593-2600.

[12] Qiu X J, Huang J Q, Lu F, et al. Fault diagnosis and isolation of the component and sensor for aircraft engine[J]. Journal of Aerospace Power, 2012, 27(6): 1432-1440.

[13] Zhang S G, Guo Y Q, Lu J. Aircraft engine sensor fault diagnostics through dual-channel sensor measurements based on a bank of hybrid Kalman filters[J]. Computer Measurement & Control, 2012, 20(1): 21-24.(in Chinese) 张书刚, 郭迎清, 陆军. 基于混合卡尔曼滤波器组的航空发动机双通道传感器故障检测[J]. 计算机测量与控制, 2012, 20(1): 21-24.

[14] Kobayashi T, Simon D L. Hybrid Kalman filter approach for aircraft engine in-flight diagnostics: sensor fault detection case, NASA/TM-2006-214418[R]. Cleveland, OH: NASA, 2006.

[15] Kobayashi T. Aircraft engine sensor/actuator/component fault diagnosis using a bank of Kalman filters, NASA/CR-2003-212298[R]. Cleveland, OH: NASA, 2003.

[16] Kobayashi T, Simon D L. Evaluation of an enhanced bank of Kalman filters for in-flight aircraft engine sensor fault diagnostics, NASA/TM-2004-213203[R]. Cleveland, OH: NASA, 2004.

[17] Zhang S G, Guo Y Q, Lu J. Development of aircraft engine component-level models based on GasTurb/MATLAB[J]. Journal of Aerospace Power, 2012, 27(12): 2850-2856.(in Chinese) 张书刚, 郭迎清, 陆军. 基于GasTurb/MATLAB的航空发动机部件级模型研究与实现[J]. 航空动力学报, 2012, 27(12): 2850-2856.

[18] Kumar A, Viassolo D. Model-based fault tolerant control, NASA/CR-2008-215273[R]. Cleveland, OH: NASA, 2008.

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