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Diesel Engine Valve Clearance Fault Diagnosis Based on Improved Variational Mode Decomposition and Bispectrum

Author

Listed:
  • Xiaoyang Bi

    (Department of Mechanics, Tianjin University, Tianjin 300354, China
    Department of Industrial Technology, California State University, Fresno, CA 93740, USA
    Tianjin Key Laboratory of Nonlinear Dynamics and Control, Tianjin 300354, China
    National Demonstration Center for Experimental Mechanics Education (Tianjin University), Tianjin 300354, China)

  • Shuqian Cao

    (Department of Mechanics, Tianjin University, Tianjin 300354, China
    Tianjin Key Laboratory of Nonlinear Dynamics and Control, Tianjin 300354, China
    National Demonstration Center for Experimental Mechanics Education (Tianjin University), Tianjin 300354, China)

  • Daming Zhang

    (Department of Industrial Technology, California State University, Fresno, CA 93740, USA)

Abstract

The evaluation and fault diagnosis of a diesel engine’s health conditions without disassembly are very important for diesel engine safe operation. Currently, the research on fault diagnosis has focused on the time domain or frequency domain processing of vibration signals. However, early fault signals are mostly weak energy signals, and the fault information cannot be completely extracted by time domain and frequency domain analysis. Thus, in this article, a novel fault diagnosis method of diesel engine valve clearance using the improved variational mode decomposition (VMD) and bispectrum algorithm is proposed. First, the experimental study was designed to obtain fault vibration signals. The improved VMD method by choosing the optimal decomposition layers is applied to denoise vibration signals. Then the bispectrum analysis of the reconstructed signal after VMD decomposition is carried out. The results show that bispectrum image under different working conditions exhibits obviously different characteristics respectively. At last, the diagonal projection method proposed in this paper was used to process the bispectrum image, and the fourth order cumulant is calculated. The calculation results show that three states of the valve clearance are successfully distinguished.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:661-:d:207077
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    Citations

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    Cited by:

    1. Xiao Yang & Fengrong Bi & Yabing Jing & Xin Li & Guichang Zhang, 2022. "A Condition-Monitoring Approach for Diesel Engines Based on an Adaptive VMD and Sparse Representation Theory," Energies, MDPI, vol. 15(9), pages 1-20, May.
    2. 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.

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