Aircraft Engine Fault Diagnosis Based on Flight Process Data

被引：0
|
作者：
Ma S. [1 ]
Wu Y.-F. [1 ]
Zheng H. [1 ]
Gou L.-F. [1 ]
机构：
[1] School of Power and Energy, Northwestern Polytechnical University, Xi’an
来源：
Tuijin Jishu/Journal of Propulsion Technology | 2023年 / 44卷 / 05期
关键词：
Aircraft engine; Deep neural network; Fault diagnosis; Flight process data; GRU network;
D O I：
10.13675/j.cnki.tjjs.2208041
中图分类号：
学科分类号：
摘要：
Aiming at the data of the aircraft engine flight process，a data-driven aircraft engine fault diag⁃ nosis structure is proposed by combining the gated recurrent unit（GRU）dynamic network and the deep neural network（DNN）. Firstly，the engine health data was extracted from the flight data，and the dynamic model of the engine in a healthy state was established through a group of GRU networks. Secondly，the residual signal was gen⁃ erated by the predicted value of the GRU dynamic models and the real measurement signal，and the residual sig⁃ nal was used as the input of the DNN network to predict the engine health parameters. Finally，the engine fault detection and identification were realized by the diagnostic decision module. The proposed fault diagnosis system was verified by using the real flight condition data set of the engine generated by simulation. The results show that compared with the direct use of sensor measurement data，the residual structure based on GRU network can great⁃ ly improve the performance of fault detection and identification，and the fault detection accuracy and fault identi⁃ fication accuracy can reach 96.51% and 95.06%. The dependence on the number of training data samples is small，and good prediction results can be obtained with few training samples. © 2023 Journal of Propulsion Technology. All rights reserved.
引用
收藏
相关论文
共 28 条
  • [1] Volponi A J., Gas Turbine Engine Health Management：Past，Present，and Future Trends［J］, Journal of Engineering for Gas Turbines and Power, 136, 5, (2014)
  • [2] Vanini Z N S，, Khorasani K，, Meskin N., Fault Detection and Isolation of a Dual Spool Gas Turbine Engine Using Dynamic Neural Networks and Multiple Model Approach ［J］, Information Sciences, 259, pp. 234-251, (2014)
  • [3] 9
  • [4] 43, 9
  • [5] 34, 3, (2019)
  • [6] Chao M A，, Kulkarni C S，, Goebel K，, Et al., Hybrid Deep Fault Detection and Isolation：Combining Deep Neural Networks and System Performance Models［J］, International Journal of Prognostics and Health Management, (2020)
  • [7] Chao M A，, Kulkarni C，, Goebel K，, Et al., Fusing Phys⁃ ics-Based and Deep Learning Models for Prognostics［J］, Reliability Engineering & System Safety, 217, (2022)
  • [8] Li M, Chen Z，, Et al., A Data-Driven Residual-based Method for Fault Diagnosis and Isolation in Wind Turbines［J］, IEEE Transactions on Sustainable Energy, 10, 2, pp. 895-904, (2018)
  • [9] Oh H，, Lee W Y，, Won J，, Et al., Residual-Based Fault Diagnosis for Thermal Management Systems of Proton Ex⁃ change Membrane Fuel Cells［J］, Applied Energy, 277, (2020)
  • [10] Tayarani-Bathaie S S，, Khorasani K., Fault Detection and Isolation of Gas Turbine Engines Using a Bank of Neural Networks［J］, Journal of Process Control, 36, pp. 22-41, (2015)
123