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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2014, Vol. 35 ›› Issue (6): 1612-1622.doi: 10.7527/S1000-6893.2013.0543

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles     Next Articles

Research on Gas Fault Fusion Diagnosis of Aero-engine Component

LI Yebo1, LI Qiuhong1, HUANG Xianghua1, ZHAO Yongping2   

  1. 1. Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
  • Received:2013-07-09 Revised:2014-02-18 Online:2014-06-25 Published:2014-06-20
  • Supported by:

    National Natural Science Foundation of China(51006052,61104067); Aeronautical Science Foundation of China(20110652003); The Fundamental Research Funds for Central Universities(NZ2012104); Graduate Student Research and Innovation Program of Jiangsu Province(CXZZ12_0169)

Abstract:

A model-based and data-driven-based aero-engine component fault fusion diagnosis method is presented in this paper. The extreme learning machine (ELM) is used to the data-driven-based component fault diagnosis. However, the ELM input weights and hidden layer biases are generated randomly, which usually leads to more nodes in the hidden layer and poor generalization ability. To overcome these drawbacks of ELM, an improved differential evolution (IDE) is used to optimize the input weights and the hidden layer biases. The optimization is carried out according to the root mean squared error (RMSE) of the validation set and the norm of the output weights. Meantime, singular value decomposition (SVD)-based Reduced-dimensional Kalman (SVD-Kalman) filters are used to estimate the health parameters which can solve the problem of limited number of sensors in the model based diagnosis method. To improve the accuracy of fault diagnosis for an aero-engine component, the prediction results of health parameters based on both methods are fused by the improved recursive reduced least squares support vector regression (IRR-LSSVR). Simulation results show that compared with either the model-based or data-driven-based approach, the proposed fusion method improves the accuracy of fault diagnosis significantly.

Key words: aero-engine, extreme learning machine, component fault, differential evolution, Kalman filter, information fusion

CLC Number: