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

<div>The diagnosis results of testing set of four cases with the use of TSFRS and different dimensionality reduction methods. The output dimension sizes of PCA, LDA, LFDA, MMC, and LMMC are 20, 11, 11, 11, and 11, respectively.</div>

Shock and Vibration

fig26

Figure 26

Figure 26: Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition 

Figure 26 | Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition 