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Analytical Redundancy Design for Aeroengine Sensor Fault Diagnostics Based on SROS-ELM

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  • Jun Zhou
  • Yuan Liu
  • Tianhong Zhang

Abstract

Analytical redundancy technique is of great importance to guarantee the reliability and safety of aircraft engine system. In this paper, a machine learning based aeroengine sensor analytical redundancy technique is developed and verified through hardware-in-the-loop (HIL) simulation. The modified online sequential extreme learning machine, selective updating regularized online sequential extreme learning machine (SROS-ELM), is employed to train the model online and estimate sensor measurements. It selectively updates the output weights of neural networks according to the prediction accuracy and the norm of output weight vector, tackles the problems of singularity and ill-posedness by regularization, and adopts a dual activation function in the hidden nodes combing neural and wavelet theory to enhance prediction capability. The experimental results verify the good generalization performance of SROS-ELM and show that the developed analytical redundancy technique for aeroengine sensor fault diagnosis based on SROS-ELM is effective and feasible.

Suggested Citation

  • Jun Zhou & Yuan Liu & Tianhong Zhang, 2016. "Analytical Redundancy Design for Aeroengine Sensor Fault Diagnostics Based on SROS-ELM," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, April.
  • Handle: RePEc:hin:jnlmpe:8153282
    DOI: 10.1155/2016/8153282
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    1. Veerasamy, Gomathi & Kannan, Ramkumar & Siddharthan, RakeshKumar & Muralidharan, Guruprasath & Sivanandam, Venkatesh & Amirtharajan, Rengarajan, 2022. "Integration of genetic algorithm tuned adaptive fading memory Kalman filter with model predictive controller for active fault-tolerant control of cement kiln under sensor faults with inaccurate noise ," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 191(C), pages 256-277.
    2. Zhang, Xinhai & Wang, Kang & Geng, Jia & Li, Ming & Song, Zhiping, 2024. "A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss," Energy, Elsevier, vol. 294(C).
    3. Xu, Maojun & Liu, Jinxin & Li, Ming & Geng, Jia & Wu, Yun & Song, Zhiping, 2022. "Improved hybrid modeling method with input and output self-tuning for gas turbine engine," Energy, Elsevier, vol. 238(PA).

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