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Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle

Author

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  • Junjie Lu

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Jinquan Huang

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Feng Lu

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming data chunk usually possesses a characteristic of timeliness, and the overdue training data may mislead the subsequent learning process. The proposed MOS-ELM can improve the training process by introducing the concept of memory principle into the online sequential extreme learning machine (OS-ELM) to tackle the timeliness of the data chunk. Simulations on some time series problems and some benchmark databases show that MOS-ELM performs better in generalization performance, stability, and prediction accuracy than OS-ELM. The experiment results of the MOS-ELM-based sensor fault diagnosis system also verify the excellent generalization performance of MOS-ELM and indicate the effectiveness and feasibility of the developed diagnosis system.

Suggested Citation

  • Junjie Lu & Jinquan Huang & Feng Lu, 2017. "Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle," Energies, MDPI, vol. 10(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:39-:d:86693
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    References listed on IDEAS

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    1. Abul Kalam Azad & Mohammad Golam Rasul & Talal Yusaf, 2014. "Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications," Energies, MDPI, vol. 7(5), pages 1-30, May.
    2. Ogaji, S. O. T. & Singh, R. & Probert, S. D., 2002. "Multiple-sensor fault-diagnoses for a 2-shaft stationary gas-turbine," Applied Energy, Elsevier, vol. 71(4), pages 321-339, April.
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    Cited by:

    1. Hang Liu & Youyuan Wang & Yi Yang & Ruijin Liao & Yujie Geng & Liwei Zhou, 2017. "A Failure Probability Calculation Method for Power Equipment Based on Multi-Characteristic Parameters," Energies, MDPI, vol. 10(5), pages 1-15, May.
    2. Jie Liu & Qiu Tang & Wei Qiu & Jun Ma & Junfeng Duan, 2021. "Probability-Based Failure Evaluation for Power Measuring Equipment," Energies, MDPI, vol. 14(12), pages 1-16, June.
    3. Jiao Liu & Jinfu Liu & Daren Yu & Myeongsu Kang & Weizhong Yan & Zhongqi Wang & Michael G. Pecht, 2018. "Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network," Energies, MDPI, vol. 11(8), pages 1-18, August.
    4. Ferhat Ucar & Omer F. Alcin & Besir Dandil & Fikret Ata, 2018. "Power Quality Event Detection Using a Fast Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-14, January.
    5. 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).
    6. Yanfeng He & Zhijie Guo & Xiang Wang & Waheed Abdul, 2023. "A Hybrid Approach of the Deep Learning Method and Rule-Based Method for Fault Diagnosis of Sucker Rod Pumping Wells," Energies, MDPI, vol. 16(7), pages 1-19, March.

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