Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (12): 3831-3839.doi: 10.13229/j.cnki.jdxbgxb.20240528

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Small sample rolling bearing compound fault diagnosis based on improved VME and mRVM

Zhi-gang FENG1(),Zhi-yuan ZHANG1,Bing DONG2,Ming-yue YU1   

  1. 1.Scholl of Automation，Shenyang Aerospace University，Shenyang 110136，China
    2.Shenyang Aircraft Industry （Group） Co. ，Ltd. ，Shenyang 110000，China
  • Received:2024-05-11 Online:2025-12-01 Published:2026-02-03

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Abstract:

To address the problem that traditional signal separation algorithms cannot efficiently and accurately analyze specific faults, a signal extraction method combining Variational Mode Decomposition (VMD), Laplacian Energy (LE) and Variational Mode Extraction (VME) was proposed, and multi-class Relevance Vector Machine (mRVM) together with Dempster-Shafer (DS) evidence theory was adopted for intelligent fault diagnosis. This method is dedicated to the small-sample data scenario. First, the VMD-LE-VME method is used to extract effective fault information from fault signals and obtain multi-domain features. Second, the multi-domain features are input into the mRVM for fault identification. Finally, the classification results are fused by means of DS evidence theory to derive the final diagnosis results. Experimental results verify the effectiveness and superiority of the proposed method in handling small-sample data.

Key words: fault diagnosis, variational mode decomposition, Laplacian energy, variational mode extraction, multiclass relevance vector machine, Dempster-Shafer evidence theory

CLC Number: 

  • TH17

Fig.1

Flowchart of the proposed method"

Fig.2

VMD-LE-VME conceptual diagram"

Table 1

Multidomain features"

特征类型特征名称

时域

特征

最大值最小值平均值中位数均方根

波形

因子

峰值

因子

脉冲

因子

裕度

因子

峭度

频域

特征

重心

频率

均方

频率

均方根

频率

谱峭度

均值

熵值

特征

奇异

谱熵

功率

谱熵

近似熵样本熵能量熵

Fig.3

Steps for establishing fusion rules"

Fig.4

Aircraft engine rotor rolling bearing test bench"

Fig.5

Composite fault of rolling bearings"

Fig.6

IMF components after VMD decomposition"

Table 2

LE indicators of corresponding graph signals for each IMF"

IMF分量LE
IMF10.075 9
IMF20.082 1
IMF30.051 3
IMF47.813 2

Fig.7

Spectrum diagram of IMF3"

Fig.8

VME extraction rendering"

Table 3

Diagnostic accuracy of mRVM under two types of feature inputs"

特征

类型

方案
ABCDEF

时域

频域

9879.2568.507873.7579
熵值1008670.2588.2580.5082.75

Table 4

Final accuracy after DS evidence theory"

方案ABCDEF
准确率9982.6269.3883.1377.1380.88

Fig.9

Confusion matrix"

Table 5

Diagnostic accuracy of mRVM under different proportions of training and testing sets"

特征类型训练集与测试集比例
6∶45∶54∶63∶72∶8
时域频域94.6791.6788.3386.6786.25
熵值99.3397.5092.2291.4390

Table 6

Diagnostic accuracy of different models"

特征

类型

mRVMSVM

LS

SVM

ELMRBFCNN

时域

频域

9880.5082.5387.2530.7590.25
熵值10088.3190.159034.7794.73

Table 7

Final accuracy after DS evidence theory"

模型mRVMSVMLSSVMELMRBFCNN
准确率10088.3190.159034.7794.73
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