Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (2): 318-328.doi: 10.13229/j.cnki.jdxbgxb20200856

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Multi⁃fault diagnosis of rolling bearing based on adaptive variational modal decomposition and integrated extreme learning machine

Jin-hua WANG1,2,3(),Jia-wei HU1,Jie CAO1,4,Tao HUANG5   

  1. 1.College of Electrical & Information Engineering，Lanzhou University of Technology，Lanzhou 730050，China
    2.Key Laboratory of Gansu Advanced Control for Industrial Processes，Lanzhou University of Technology，Lanzhou 730050，China
    3.National Experimental Teaching Center of Electrical and Control Engineering，Lanzhou University of Technology，Lanzhou 730050，China
    4.Engineering Research Center of Manufacturing Information of Gansu Province，Lanzhou 730050，China
    5.China Municipal Engineering Northwest Design and Research Institute Co. ，Ltd. ，Lanzhou 730000，China
  • Received:2020-11-06 Online:2022-02-01 Published:2022-02-17

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Abstract:

In view of the difficulty of feature extraction and low classification accuracy in the diagnosis of rolling bearing multiple faults， this paper starts from the two aspects of effective feature extraction and fault classification accuracy， and combines the method of variational modal decomposition （VMD） and extreme learning machine （ELM）. An adaptive method for diagnosing multiple faults of rolling bearings is presented. Aiming at the situation that VMD parameters need to be manually set in advance， which leads to poor signal decomposition， the Gray Wolf Algorithm （GWO） is proposed to optimize VMD to achieve adaptively obtaining the best decomposition parameters k and α. Furthermore， in order to overcome the problem of low classification accuracy of a single ELM model and unstable classification results， an integrated extreme learning machine （IELM） is proposed to realize the classification and recognition of multiple faults， and improve the accuracy and stability of fault classification. First， use GWO to optimize VMD and obtain the best decomposition parameters adaptively； Secondly， select and extract the time-frequency feature vector of the modal signal； Finally， input the feature vector into IELM for training and classification. Experiments show that this method can adaptively decompose signals and produce the best decomposing effect， realizing accurate early warning and identification of rolling bearing faults.

Key words: fault diagnosis, gray wolf optimization, variational mode decomposition, integrated extreme learning machine, rolling bearing

CLC Number: 

  • TP277

Fig.1

GWOVMD algorithm for flow chart"

Fig.2

Simulation signal diagram"

Fig.3

Mode of EMD decomposition"

Fig.4

Mode of EEMD decomposition"

Fig.5

Mode of VMD decomposition optimized by GWO"

Fig.6

Flow chart of rolling bearing fault diagnosis based on adaptive VMD and IELM"

Fig.7

Test platform"

Table 1

Data set"

数据集类型负载/HP转速/（r·min-1）故障尺寸/cm
A正常217500.017 78
内圈
滚动体
外圈
B内圈017970.017 78
11772
21750
31730
C内圈217500.017 78
0.035 56
0.053 34
0.071 12

Table 2

GWOVMD decomposition of best parameters"

状态kα
正常94860
内圈4585
滚动体65170
外圈106726

Table 3

Correlation coefficient screening modal signal"

项目正常内圈外圈滚动体
IMF10.510.270.050.13
IMF20.290.410.080.24
IMF30.780.660.070.47
IMF40.220.630.290.59
IMF50.09-0.370.67
IMF60.05-0.390.16
IMF70.04-0.65-
IMF80.03-0.62-
IMF90.03-0.08-
IMF10--0.08-

Fig.8

Test sample classification accuracy of data set"

Table 4

Accuracies of five fault diagnosis methods %"

方法数据集A数据集B数据集C
EEMD-ELM93.7591.2588.75
EEMD-IELM98.7597.5093.75
自适应VMD-ELM97.5096.2590.00
文献［30］98.7596.2590.00
自适应VMD-IELM100.00100.0098.75
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