A.11: DFDD: Difference between revisions
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• '''Reference, URL''': https://ieeexplore.ieee.org/document/8598879 | • '''Reference, URL''': https://ieeexplore.ieee.org/document/8598879 | ||
• ''' | • '''Applicable Business Category'': Manufacturing | ||
• '''Application in relevant Projects/Initiatives''': | • '''Application in relevant Projects/Initiatives''': | ||
• '''Type''': ML Model | • '''Asset Type''': ML Model | ||
• '''AI Breadth''': | • '''AI Breadth''': ML | ||
• '''Learning Ability''': Deep Transfer Learning | • '''Learning Ability''': Deep Transfer Learning | ||
• '''Related technologies''': | • '''Related technologies''': Digital Twin, Deep Learning, Fault Diagnosis, Real Time analytic | ||
• '''Applicable Research Area''': Integrative AI | |||
• '''Applicable Technical Category''': Machine Learning | |||
• '''License Information''': GNU General Public License version 3 | • '''License Information''': GNU General Public License version 3 | ||
Latest revision as of 08:55, 27 December 2024
• Short Description: A two-phase, digital-twin-assisted fault diagnosis method which is using deep transfer learning fault diagnosis both in the development and maintenance phases.
• Reference, URL: https://ieeexplore.ieee.org/document/8598879
• 'Applicable Business Category: Manufacturing
• Application in relevant Projects/Initiatives:
• Asset Type: ML Model
• AI Breadth: ML
• Learning Ability: Deep Transfer Learning
• Related technologies: Digital Twin, Deep Learning, Fault Diagnosis, Real Time analytic
• Applicable Research Area: Integrative AI
• Applicable Technical Category: Machine Learning
• License Information: GNU General Public License version 3
• Related to circularity and sustainability: Yes_Fault Diagnosis/Eco-Design
• Audience: Developers
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