Research Article
Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition
Figure 12
The curve representation of diagnosis results obtained by OFS-TSFRS-LFDA-SVM using DTCWPT with different dimension sizes for LFDA. The LFDA (5) represents that the number of dimension size is 5.
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