A fusion TFDAN-Based framework for rotating machinery fault diagnosis under noisy labels

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Abstract
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Funding Information
  • Hebei Provincial Natural Science Foundation (E2022203022)
  • Department of Education of Hebei Province (ZD2022052)

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