umbertogriffo/Predictive-Maintenance-using-LSTM
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
This project helps operations engineers and maintenance planners predict when critical equipment, like aircraft engines, are likely to fail. By analyzing historical sensor data from these machines, it generates predictions for the remaining useful life or flags if a failure is imminent within a specific timeframe. This allows for proactive maintenance scheduling, reducing unexpected downtime and costs.
722 stars. No commits in the last 6 months.
Use this if you need to forecast equipment failures based on operational sensor data to optimize maintenance schedules and prevent unexpected breakdowns.
Not ideal if you lack historical sensor data for your equipment or need to diagnose the root cause of failures rather than just predicting them.
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722
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250
Language
Python
License
MIT
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Last pushed
Feb 12, 2024
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