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

<div>The curve representation of the diagnosis results of OFS-TSFRS-SVM using DTCWPT with different sfn.</div>

Shock and Vibration

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Figure 6

Figure 6: Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition 

Figure 6 | Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition 