Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition

<div>The curve representation of diagnosis results obtained by OFS-TSFRS-PCA-SVM using WPT with different dimension sizes for PCA. The PC represents the number of dimension size.</div>

Shock and Vibration

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Figure 9

Figure 9: Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition 

Figure 9 | Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition 