This project is demonstration of a real-time predictive maintenance model application. The project is comprised of the following three parts:
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A Python implementation of this popular R notebook that contains the steps of implementing a predictive maintenance model: Link
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A Python app to simulate a real time feed of simulated failure, maintainence and condition data. Specifically, this app will involve the following: (In Progress)
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With the given time-series data, generate simulated data using the "block bootstrap" method.
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Post simulated data to Google Cloud Function end points
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A set of Google Cloud Function to do the following: (In Progress)
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Perform feature engineering on the simulated data
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Generate failure predictions for simulated data
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A Google Datastudio dashboard to visualize the simulated data and predictions: (In Progress)