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Aero-engine Gas Path Performance Degradation Assessment Based on a Multi-objective Optimized Discrete Feedback Network

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  • Control Theory and Applications
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Abstract

In order to solve the problem of neural network algorithm for aero-engine’s gas path performance evaluation under high-dimensional evaluation index with non-equal weights, the trend analysis method and fault fingerprints are used to mine engine’s gas path performance characteristic parameters. A comprehensive weighting method based on game theory is proposed to optimize the weight value of each gas path performance characteristic parameter. A discrete feedback neural network with single-layer and binary output is established. The original gas path performance evaluation index is equivalently expanded according to the weight ratio, and the gas path state evaluation indexes with different weights are mapped into higher-dimensional equivalent evaluation indexes with equal weights. The network attractor is designed according to the engine gas path performance evaluation levels, and the design of discrete feedback neural network weights is transformed into multi-objective programming problem, and a particle swarm optimization algorithm with adaptive inertia weight is used to improve the efficiency and global search ability of particle swarm optimization. The experimental results shows that the proposed model and algorithm can provide a scientific and reasonable machine learning method for the evaluation of high-dimensional evaluation index with non-equal weights.

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Authors and Affiliations

  1. School of Automotive Engineering, Harbin Institute of Technology at Weihai, No. 2 Wenhuaxi Road, Weihai, 264209, China

    Zhi-Quan Cui

  2. School of Naval Architecture and Ocean Engineering, Harbin Institute of Technology at Weihai, No. 2 Wenhuaxi Road, Weihai, 264209, China

    Shi-Sheng Zhong

  3. School of Mechanical and Electrical Engineering, Harbin Institute of Technology, No. 92 West Dazhi Street, Harbin, 150001, China

    Zhi-Qi Yan

Authors
  1. Zhi-Quan Cui
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Correspondence to Zhi-Quan Cui.

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This work was supported by National Natural Science Foundation of China, grant number U1733201 and by Fundamental Research Funds for the Central Universities, grant number HIT.NSRIF.2016-IDGA18102116.

Zhi-Quan Cui received his M.Sc degree in automotive engineering from Harbin Institute of Technology at Weihai, Weihai, China, and his Ph.D. degree in mechanical design and theory from Harbin Institute of Technology, Harbin, China, in 2004 and 2013, respectively. His research interests include mechanical and electrical equipment condition monitoring, equipment fault diagnosis, new energy vehicles, and driverless vehicles.

Shi-Sheng Zhong received his M.E. degree in mechanical engineering from Harbin Institute of Technology, Harbin, China, and his Ph.D. degree in mechanical engineering from Huazhong University of Science and Technology, Wuhan, China, in 1992 and 1995, respectively. He is currently a Professor and Ph.D. Supervisor of mechanical engineering with the School of Naval Architecture and Ocean Engineering, Harbin Institute of Technology at Weihai, Weihai, China. His main research interests include intelligent manufacturing, prognostics and health management, and maintenance, repair and overhaul.

Zhi-Qi Yan received his M.Sc degree in mechanical engineering from Civil Aviation University of China, Tianjin, China in 2017. Now he is studying for a doctorate degree in mechanical design and theory in Harbin Institute of Technology. His research interests include engine-washing, optimization methods, prognostics and health management.

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Cui, ZQ., Zhong, SS. & Yan, ZQ. Aero-engine Gas Path Performance Degradation Assessment Based on a Multi-objective Optimized Discrete Feedback Network. Int. J. Control Autom. Syst. 19, 2079–2091 (2021). https://doi.org/10.1007/s12555-019-1081-6

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  • DOI: https://doi.org/10.1007/s12555-019-1081-6

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