Robust Bearing Fault Diagnosis Using Transfer Learning and SISA-Based Machine Unlearning
A transferred SISA (Sharded, Isolated, Sliced, and Aggregated) fault diagnosis framework is developed and applied to rolling bearing data, demonstrating a 84.32% decrease in retraining time compared to non-SISA full-retraining while restoring accuracy to the pre-poisoning SISA level.
Emily Yin, Jingyi Yan, Nanhong Liu et al.
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