Research Article

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

Figure 13

The curve representation of diagnosis results obtained by OFS-TSFRS-LFDA-SVM using WPT with different dimension sizes for LFDA. The LFDA (5) represents that the number of dimension size is 5.
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