IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p1205-d743636.html
   My bibliography  Save this article

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

Author

Listed:
  • Xin Li

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China)

  • Fengrong Bi

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China)

  • Lipeng Zhang

    (Motorcycle Design Institute, Tianjin Internal Combustion Engine Research Institute, Tianjin 300072, China)

  • Xiao Yang

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China)

  • Guichang Zhang

    (College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China)

Abstract

This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and the gated recurrent unit (GRU), this novel method displays superior performance and could benefit the fault end-to-end detection of rotating machinery.

Suggested Citation

  • Xin Li & Fengrong Bi & Lipeng Zhang & Xiao Yang & Guichang Zhang, 2022. "An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer," Energies, MDPI, vol. 15(3), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1205-:d:743636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/1205/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/1205/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hirose, Kosuke & Matsumura, Toshihiro, 2020. "A comparison between emission intensity and emission cap regulations," Energy Policy, Elsevier, vol. 137(C).
    2. Xiaoyang Bi & Shuqian Cao & Daming Zhang, 2019. "Diesel Engine Valve Clearance Fault Diagnosis Based on Improved Variational Mode Decomposition and Bispectrum," Energies, MDPI, vol. 12(4), pages 1-16, February.
    3. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    4. Heng-di Wang & Si-er Deng & Jian-xi Yang & Hui Liao & Wen-bo Li, 2020. "Parameter-Adaptive VMD Method Based on BAS Optimization Algorithm for Incipient Bearing Fault Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-15, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Yanfang & Wei, Jinpeng & Gao, Qi & Shi, Xunpeng & Zhou, Dequn, 2022. "Coordination between the energy-consumption permit trading scheme and carbon emissions trading: Evidence from China," Energy Economics, Elsevier, vol. 116(C).
    2. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    3. Hirose, Kosuke & Ishihara, Akifumi & Matsumura, Toshihiro, 2021. "Tax versus Regulations: Robustness to Polluter Lobbying Against Near-Zero Emission Targets," MPRA Paper 108380, University Library of Munich, Germany.
    4. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    5. 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.
    6. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    7. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    8. Xing, Mingqing & Tan, Tingting & Wang, Xia, 2021. "Emission taxes and environmental R&D risk choices in a duopoly market," Economic Modelling, Elsevier, vol. 101(C).
    9. Yunpeng Cao & Xinran Lv & Guodong Han & Junqi Luan & Shuying Li, 2019. "Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network," Energies, MDPI, vol. 12(24), pages 1-17, December.
    10. Kim, Min Jae & Kim, Jeong Ho & Kim, Tong Seop, 2018. "The effects of internal leakage on the performance of a micro gas turbine," Applied Energy, Elsevier, vol. 212(C), pages 175-184.
    11. Kiki Ayu & Akilu Yunusa-Kaltungo, 2020. "A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems," Energies, MDPI, vol. 13(8), pages 1-32, April.
    12. Kang, Do Won & Kim, Tong Seop, 2018. "Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation," Applied Energy, Elsevier, vol. 212(C), pages 1345-1359.
    13. Huang, Yufeng & Tao, Jun & Zhao, Junyi & Sun, Gang & Yin, Kai & Zhai, Junyi, 2023. "Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine," Energy, Elsevier, vol. 283(C).
    14. Hirose, Kosuke & Matsumura, Toshihiro, 2022. "Common ownership and environmental Corporate Social Responsibility," Energy Economics, Elsevier, vol. 114(C).
    15. Ming Cheng & Qiang Zhang & Yue Cao, 2024. "An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model," Energies, MDPI, vol. 17(15), pages 1-16, July.
    16. Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).
    17. Tianyu Luo & Hongmin Chen, 2023. "A Comparative Analysis of Separate and Joint Environmental Rights Trading Markets in China," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    18. Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Liu, Jiao & Yu, Daren, 2021. "Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers," Applied Energy, Elsevier, vol. 302(C).
    19. Shun Dai & Xiaoyi Zhang & Mingyu Luo, 2024. "A Novel Data-Driven Approach for Predicting the Performance Degradation of a Gas Turbine," Energies, MDPI, vol. 17(4), pages 1-17, February.
    20. Jinfu Liu & Zhenhua Long & Mingliang Bai & Linhai Zhu & Daren Yu, 2021. "A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems," Energies, MDPI, vol. 14(2), pages 1-31, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1205-:d:743636. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.