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VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals

Author

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  • Xiaobo Bi

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
    School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China)

  • Jiansheng Lin

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China)

  • Daijie Tang

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China)

  • Fengrong Bi

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China)

  • Xin Li

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China)

  • Xiao Yang

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China)

  • Teng Ma

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China)

  • Pengfei Shen

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China)

Abstract

Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classification effect. In this paper, the diesel engine fault simulation experiment was carried out to simulate various faults including valve clearance fault, fuel supply fault and common rail pressure fault. Each kind of machine fault varies in different degrees. To prove the effectiveness of VMD-KFCM, the proposed method is compared with empirical mode decomposition (EMD)-KFCM, ensemble empirical mode decomposition (EEMD)-KFCM, VMD-back propagation neural network (BPNN), and VMD-deep belief network (DBN). Results show that VMD-KFCM has advantages in accuracy, simplicity, and efficiency. Therefore, the method proposed in this paper can be used for diesel engine fault diagnosis, and has good application prospects.

Suggested Citation

  • Xiaobo Bi & Jiansheng Lin & Daijie Tang & Fengrong Bi & Xin Li & Xiao Yang & Teng Ma & Pengfei Shen, 2020. "VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals," Energies, MDPI, vol. 13(1), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:228-:d:304670
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    References listed on IDEAS

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    1. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    2. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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    Cited by:

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