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

Xiaobo Bi, Jiansheng Lin, Daijie Tang, Fengrong Bi, Xin Li, Xiao Yang, Teng Ma and Pengfei Shen
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Xiaobo Bi: State Key Laboratory of Engines, Tianjin University, Tianjin 300072, 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

Energies, 2020, vol. 13, issue 1, 1-20

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.

Keywords: diesel engine; fault diagnosis; variational mode decomposition; kernel-based fuzzy c-means clustering; empirical mode decomposition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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