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Condition monitoring and predictive maintenance of electrical assets using deep learning
We deal with condition monitoring and predictive maintenance of a selected electrical asset using deep learning. We propose an efficient predictive maintenance strategy for a selected electrical asset in order to optimize their life cycle costs. We will investigate the use of LSTM networks in order to predict the Remaining Useful Life RUL for the selected equipment.
Faculty Supervisor:
Mustapha Nour El Fath;Sofiane Achiche
Student:
Partner:
GE Renewable Energy
Discipline:
Engineering
Sector:
Manufacturing; Other services (except public administration); Utilities
University:
Université Laval
Program:
Accelerate
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