Deep learning has proven to be an effective tool in predictive analytics due to its ability to understand patterns in large data sets. Supervised Deep learning techniques are utilized to understand the relationships between electric motor aging and the sensor parameters from the same. This approach overcomes the challenge of the requirements of high quality labeled data which are both time consuming and difficult to acquire. Thus, use of supervised deep learning methods has the potential to deploy RUL models with minimal training data. This paper focuses on the implementation of deep learning methods for Remaining Useful Life prediction for electric motor. Keywords—Deep learning, Remaining Useful Life, RUL, Feed Forward Neural Networks
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