Operationalize deep learning: How to deploy and consume your LSTM networks for predictive maintenance scenarios
Francesca Lazzeri and Fidan Boylu Uz explain how to operationalize LSTM networks to predict the remaining useful life of aircraft engines. They use simulated aircraft sensor values to predict when an aircraft engine will fail in the future so that maintenance can be planned in advance.
| Talk Title | Operationalize deep learning: How to deploy and consume your LSTM networks for predictive maintenance scenarios |
| Speakers | Francesca Lazzeri (Microsoft), Fidan Boylu Uz (Microsoft) |
| Conference | Strata Data Conference |
| Conf Tag | Big Data Expo |
| Location | San Jose, California |
| Date | March 6-8, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Deep learning has proven to show superior performance in certain domains such as object recognition and image classification. It has also gained popularity in domains such as finance where time series data plays an important role. Predictive maintenance, where data is collected over time to monitor the state of an asset with the goal of finding patterns to predict failures, also benefits from deep learning algorithms. Long short-term memory (LSTM) networks are especially appealing to the predictive maintenance domain due to the fact that they are very good at learning from sequences, making it possible to look back for longer periods of time to detect failure patterns. Francesca Lazzeri and Fidan Boylu Uz explain how to operationalize LSTM networks to predict the remaining useful life of aircraft engines. They use simulated aircraft sensor values to predict when an aircraft engine will fail in the future so that maintenance can be planned in advance. Francesca and Fidan share their data science process, from data ingestion to operationalization, in a Jupiter notebook with the Keras deep learning library with Microsoft Cognitive Toolkit CNTK as a backend.
