Maintenance decision method of a turbofan engine based on fault detection

被引：0
|
作者：
Zhang Y.-S. [1 ,2 ]
Wu C. [2 ]
Tang H.-L. [1 ]
Lin J. [2 ]
Hao M. [2 ]
机构：
[1] School of Energy and Power Engineering, Beijing University of Aeronautics and Astronautics, Beijing
[2] Technology and Training Center, The 5713 Factory the Chinese People's Liberation Army, Xiangyang, 441000, Hubei
来源：
Hangkong Dongli Xuebao/Journal of Aerospace Power | 2017年 / 32卷 / 01期
关键词：
BP neural network; Expert system; Fault detection; Maintenance decision; Turbofan engines;
D O I：
10.13224/j.cnki.jasp.2017.01.012
中图分类号：
学科分类号：
摘要：
In the maintenance process traditional excessive maintenance of turbofan engines may result in performance deterioration, long maintenance cycle and high maintenance cost. In order to solve this problem effectively, based on turbofan engine overhaul manual and maintenance technology, the fault detection process and repair mode were studied in depth, and the fault diagnosis expert system and fault diagnosis model were established based on BP(back propagation) neural network, verifying the reliability of fault diagnosis model with several sets of real performance data, with the diagnostic accuracy rate up to 95%. Secondly, the two kinds of diagnostic information were combined to develop reliable maintenance program and optimize the maintenance process. Then a maintenance decision method was put forward. By a certain type of turbofan real validation, it shows that this method can effectively eliminate the fault of high exhaust temperature, helping to improve maintenance quality and reduce maintenance cost of the engine. © 2017, Editorial Department of Journal of Aerospace Power. All right reserved.
引用
收藏
页码：82 / 88
页数：6
相关论文
共 15 条
  • [1] Giorgio M., Guida M., Pulcini G., Et al., A condition-based maintenance policy for deteriorating units: an application to the cylinder liners of marine engine, Applied Stochastic Models in Business and Industry, 31, 3, pp. 339-348, (2015)
  • [2] Zhang T., Modeling and analysis of maintenance support capability assessment of equipments in the usage phase, (2004)
  • [3] Urban L.A., Parameter selection for multiple fault diagnostics of gas turbine engines, Journal of Engineering for Power, 97, 2, pp. 225-230, (1975)
  • [4] Rong X., Methods of life prediction and mainteance decision making in civil aeroengine health management, (2008)
  • [5] Zhao F., Predictive maintenance decision research for civil aero-engine, (2011)
  • [6] Fan K., Research on Web-based aeroengine maintenance decision system, (2009)
  • [7] Volponi A.J., De Pold H., Ganguli R., Et al., The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study, Journal of Engineering for Gas Turbines and Power, 125, 4, pp. 917-924, (2003)
  • [8] Chen T., Ye Z., Sun J., Et al., Self-organizing neural network based fault diagnosis for aeroengine gas path, Acta Aeronauticaet Astronautica Sinica, 24, 1, pp. 46-48, (2003)
  • [9] Tan G., Fault diagnosis expert system application research in pump-controlled motor system based on BP neural networks, (2000)
  • [10] Jardine A.K.S., Lin D., Banjevic D., A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, 20, 7, pp. 1483-1510, (2006)
12