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-MMC-SVM using DTCWPT with different dimension sizes for MMC. The MMC (5) represents that the number of dimension size is 5.</div>

Shock and Vibration

fig14

Figure 14

Figure 14: Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition 

Figure 14 | Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition 