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Fault detection and isolation of actuator failures in jet engines using adaptive dynamic programming

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  • Kang, Haobo
  • Ma, Hongjun

Abstract

This paper presents a adaptive dynamic programming-based fault detection and isolation (FDI) scheme to detect and isolate faults in an aircraft jet engine. To this end, the weights in Actor-Critic neural networks are first tuned to learn the input-output map of the jet engine considering its multiple working modes. The convergences of the trainings in Critic-Actor neural networks are strictly proved without knowing the drift dynamics and the input dynamics in the presence of unknown nonlinearities and approximation errors. Using the residuals that are generated by measuring the difference of each network output and the measured engine output, various criteria are established for accomplishing the fault diagnosis task, that addresses the problem of fault detection and isolation of the system components. A number of simulation studies are carried out for combustion chamber of a single-spool jet engine to demonstrate and illustrate the advantages, capabilities, and performance of our proposed fault diagnosis scheme.

Suggested Citation

  • Kang, Haobo & Ma, Hongjun, 2022. "Fault detection and isolation of actuator failures in jet engines using adaptive dynamic programming," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    • Handle: RePEc:eee:apmaco:v:414:y:2022:i:c:s0096300321007487
    DOI: 10.1016/j.amc.2021.126664
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    References listed on IDEAS

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    1. Shen Yin & Xuebo Yang & Hamid Reza Karimi, 2012. "Data-Driven Adaptive Observer for Fault Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-21, October.
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    Cited by:

    1. Long, Zhenhua & Bai, Mingliang & Ren, Minghao & Liu, Jinfu & Yu, Daren, 2023. "Fault detection and isolation of aeroengine combustion chamber based on unscented Kalman filter method fusing artificial neural network," Energy, Elsevier, vol. 272(C).
    2. Gao, Ming & Niu, Yichun & Sheng, Li & Zhou, Donghua, 2022. "Quantitative analysis of incipient fault detectability for time-varying stochastic systems based on weighted moving average approach," Applied Mathematics and Computation, Elsevier, vol. 434(C).

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