Diesel Engine Fault Diagnosis Method Based on Optimized Variational Mode Decomposition and Kernel Fuzzy C-means Clustering

被引:0
作者
Bi F. [1 ]
Tang D. [1 ]
Zhang L. [2 ]
Li X. [1 ]
Ma T. [1 ]
Yang X. [1 ]
机构
[1] The State Key Laboratory of Engines, Tianjin University, Tianjin
[2] Tianjin Internal Combustion Engine Research Institute, Tianjin
来源
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2020年 / 40卷 / 05期
关键词
Diesel engine; Fault diagnosis; Kernel fuzzy C-means clustering (KFCM); Variational mode decomposition (VMD); Vibration signal;
D O I
10.16450/j.cnki.issn.1004-6801.2020.05.004
中图分类号
学科分类号
摘要
To solve the diesel engine fault diagnosis problem, a fault diagnosis method based on the combination of variational mode decomposition (VMD) and kernel fuzzy C-means clustering (KFCM) is proposed. This paper optimizes the selection of decomposition level K in VMD algorithm, and proposes an adaptive choosing method for K. Then, three key components are selected from the decomposition results of the optimized VMD algorithm to calculate the maximum singular values, which are input into the KFCM algorithm as three-dimensional eigenvectors for classification and recognition. The optimized VMD method, VMD method and empirical mode decomposition (EMD) method are used to decompose and recognize the simulated signal and the experimental data of a diesel engine. The results show that the proposed method obviously improves the accuracy of pattern recognition. The joint algorithm proposed in this paper has better application prospects. © 2020, Editorial Department of JVMD. All right reserved.
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页码:853 / 858
页数:5
相关论文
共 13 条
[1]  
LIU Min, LI Zhining, ZHANG Yingtang, Et al., Diesel engine fault diagnosis based on multi-scale kernel independent component analysis, Journal of Vibration, Measurement & Diagnosis, 37, 5, pp. 892-897, (2017)
[2]  
HUANG N E, SHEN Z, LONG S R, Et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings Mathematical Physical & Engineering Sciences, 454, pp. 903-995, (1998)
[3]  
SMITH J S., The local mean decomposition and its application to EEG perception data, Journal of the Royal Society Interface, 2, 5, pp. 443-454, (2005)
[4]  
HU Ronghua, LOU Peihuang, TANG Dunbing, Et al., Fault diagnosis of rolling bearings based on EMD and parameter adaptive support vector machine, Computer Integrated Manufacturing Systems, 2, pp. 438-447, (2013)
[5]  
ZHANG Chao, CHEN Jianjun, GUO Xun, A gear fault diagnosis method based on EMD energy entropy and SVM, Journal of Vibration and Shock, 29, 10, pp. 216-220, (2010)
[6]  
LIU X F, BO L, LUO H L., Bearing faults diagnostics based on hybrid LS-SVM and EMD method, Measurement, 59, pp. 145-166, (2015)
[7]  
ZHENG Z, JIANG W L, WANG Z W, Et al., Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions, Mechanism & Machine Theory, 91, pp. 151-167, (2015)
[8]  
HAO Rujiang, LI Fei, A new method to suppress the EMD endpoint effect, Journal of Vibration, Measurement & Diagnosis, 38, 2, pp. 341-345, (2018)
[9]  
DRAGOMIRETSKIY K, ZOSSO D., Variational mode decomposition, IEEE Tran on Signal Processing, 62, 3, pp. 531-544, (2014)
[10]  
LIN K P., A novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm, Fuzzy Systems IEEE Transaetions on, 22, 5, pp. 1074-1087, (2014)