Optimization of Adversarial Reprogramming for Transfer Learning on Closed Box Models

Optimization of Adversarial Reprogramming for Transfer Learning on Closed Box Models

In this work, we optimise a transfer learning approach for predicting the Remaining Useful Life (RUL) of ball bearings, particularly in scenarios with limited data availability. Accurate RUL prediction is crucial for improving maintenance strategies, reducing downtime and improving machine reliabili...

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Bibliographic Details
Main Authors: Alexander Bott, Moritz Siems, Alexander Puchta, Jurgen Fleischer
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Adversial reprogramming
closed box transfer learning
machine learning
RUL-classification
Online Access:https://ieeexplore.ieee.org/document/10930470/
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https://ieeexplore.ieee.org/document/10930470/

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