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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
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    References listed on IDEAS

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