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| Transfer Learning for Machine Diagnosis and Prognosis |
| Keywords | Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault Diagnosis, Prognosis + |
|---|---|
| Level | Master + |
| OneLineSummary | Study and develop deep adversarial neural networks (DANN) based methods to detect faults and predict failures in industrial equipment, under transfer learning scenarios. + |
| Prerequisites | Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms + |
| StudentProjectStatus | Open + |
| Supervisors | Peyman Mashhadi + , Yuantao Fan + , Mohammed Ghaith Altarabichi + |
| TimeFrame | Fall 2020 + |
| Title | Transfer Learning for Machine Diagnosis and Prognosis + |
| Categories | StudentProject + |
| Modification dateThis property is a special property in this wiki. | 6 October 2020 18:32:30 + |
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