Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 80-87.doi: 10.6040/j.issn.1672-3961.0.2018.268

• Machine Learning & Data Mining • Previous Articles     Next Articles

Research onfeature selection technology in bearing fault diagnosis

Jiachen WANG1(),Xianghong TANG1,2,3,*(),Jianguang LU1,2,3   

  1. 1. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, Guizhou, China
    2. School of Mechanical Engineering, Guizhou University, Guiyang 550025, Guizhou China
    3. State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, Guizhou China
  • Received:2018-08-03 Online:2019-04-20 Published:2019-04-19
  • Contact: Xianghong TANG E-mail:wangjiachen512@qq.com;xhtang@gzu.edu.cn
  • Supported by:
    贵州省公共大数据重点实验室开放基金资助项目(2017BDKFJJ019);贵州大学引进人才基金资助项目(贵大人基合字(2016)13号)

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Abstract:

A new method based on feature selection (FS) was proposed to select efficient features to promote the classification accuracy in bearing fault diagnosis. First, the outstanding features whose classification accuracy were higher than the threshold were directly selected by diagnosis model from a big feature set. Then the significant combinations of features which had less dimensions and higher classification accuracy were selected in the candidate feature set by a distinctive feature-oriented manner. Experiments showed that the proposed method had advantages in selecting efficient features, reducing the model parameters, decreasing the demand of samples and enhancing the model classification accuracy. As a result, it provided a new idea for feature selection and improved the efficiency of bearing fault diagnosis.

Key words: rolling bearing, fault diagnosis, outstanding features, outstanding features combination, feature selection

CLC Number: 

  • TP391.4

Fig.1

Diagram of outstanding features combination"

Fig.2

Experimental test rig of CWRU"

Table 1

Samples dataset of CWRU"

轴承状态 样本个数 样本标签
正常 150 0
外圈故障 150 1
内圈故障 150 2
球体故障 150 3

Fig.3

Experimental Test Rig of CUT-2"

Fig.4

Location of 6900ZZ bearing faults"

Table 2

Samples dataset of CUT-2"

轴承状态 样本个数 样本标签
正常 200 0
外圈故障 200 1
内圈故障 200 2
球体故障 200 3

Fig.5

Bearingfault diagnosis method based on outstanding features combination"

Table 3

Set of outstanding features parameters"

诊断阶段 特征种类个数 显著特征(组合) 组合个数 人工设定准确率阈值
1 1 K、V、S、E 4 0.5
2 2 K_V、M_V、V_R、V_Ma、V_Pe、V_Pu、V_E、V_S 26 0.8
3 3 M_K_V、K_V_S、K_V_R、K_V_Ma、K_V_Pe、K_V_Pu、K_V_E、M_V_Pu、V_R_Pe、V_S_E 28 0.9
4 6 M_K_V_R、K_V_S_Ma、K_V_R_Ma、K_V_R_Pe、K_V_R_Pu、K_V_Ma_Pe(其中K_V_S_Ma和K_V_Ma_Pe准确度相同) 36 前五位
5 5 M_K_V_R_Ma、M_K_V_R_Pe、K_V_R_Ma_Pe、K_V_R_Ma_Pu 21 0.95
6 6 M_K_V_R_Ma_Pe、M_K_V_R_Ma_Pu、K_V_R_Ma_Pe_Pu 12 0.955
7 7 M_K_V_R_Ma_Pe_Pu、M_K_V_S_R_Ma_Pu、K_V_S_R_Ma_Pe_Pu 7 前三位
8 8 M_K_V_S_R_Ma_Pe_Pu
M_K_V_R_Ma_Pe_Pu_E
M_K_V_S_R_Ma_Pu_E
K_V_S_R_Ma_Pe_Pu_E		 4 前三位
9 9 M_K_V_S_R_Ma_Pe_Pu_E 诊断结束，无参数设置 诊断结束，无参数设置

Table 4

Result ofsingle feature diagnosis"

特征种类 平均分类准确率/% 是否显著特征
Mean 42.62 否
Kurtosis 63.12 是
Var 87.62 是
Skewness 57.16 是
RMS 42.12 否
Margin 41.37 否
Peak 33.37 否
Pulse 48.33 否
Edge 65.66 是

Table 5

Result oftwo types features combination diagnosis"

特征种类 平均分类准确率/% 是否显著特征组合
M_K 71.37 否
K_V 97.37 是
K_S 74.20 否
K_R 64.00 否
K_Ma 66.50 否
K_Pe 62.70 否
K_Pu 71.41 否
K_E 75.95 否
M_V 83.95 是
V_R 87.50 是
V_Ma 85.54 是
V_Pe 86.87 是
V_Pu 83.25 是
V_E 81.58 是
M_S 62.50 否
V_S 89.25 是
S_R 62.00 否
S_Ma 56.87 否
S_Pe 57.16 否
S_Pu 58.75 否
S_E 68.79 否
M_E 65.62 否
R_E 67.70 否
Ma_E 66.62 否
Pe_E 65.08 否
Pu_E 72.04 否

Table 6

Result ofthree types features combination diagnosis"

特征种类 平均分类准确率/% 是否显著特征组合
M_K_V 96.54 是
K_V_S 96.62 是
K_V_R 96.95 是
K_V_Ma 96.33 是
K_V_Pe 96.12 是
K_V_Pu 95.79 是
K_V_E 94.66 是
M_V_S 85.54 否
M_V_R 85.29 否
M_V_Ma 84.29 否
M_V_Pe 84.83 否
M_V_Pu 90.50 是
M_V_E 81.04 否
V_S_R 88.25 否
V_R_Ma 84.54 否
V_R_Pe 92.00 是
V_R_Pu 85.29 否
V_R_E 82.12 否
V_S_Ma 86.33 否
V_Ma_Pe 84.12 否
V_Ma_Pu 85.66 否
V_Ma_E 81.33 否
V_S_Pe 87.70 否
V_Pe_Pu 84.41 否
V_Pe_E 82.54 否
V_S_Pu 86.50 否
V_Pu_E 83.12 否
V_S_E 94.66 是

Fig.6

Classification accuracy trend chart of CWRU"

Fig.7

Unoutstanding features classification accuracy trendchart of CWRU"

Fig.8

Classification accuracy trend chart of CUT-2"

Fig.9

Unoutstanding features classification accuracytrend chart of CUT-2"

Table 7

Comparison of failure identification rate before and after using of outstanding features"

%
数据集 分类方法 p q
CWRU SVM 89.58 97.37
CUT-2 SVM 87.76 95.38

Fig.10

Comparison of rate of features selected"

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