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 the OFS-TSFRS-LDA-SVM model using WPT with different dimension sizes for LDA. The LDA (5) represents that the number of dimension size is 5.</div>

Shock and Vibration

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Figure 11

Figure 11: Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition 

Figure 11 | Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition 