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azure-iot-predictive-maintenance's Introduction

Microsoft Azure IoT Suite

You can deploy preconfigured solutions that implement common Internet of Things (IoT) scenarios to Microsoft Azure using your Azure subscrption. You can use preconfigured solutions:

  • as a starting point for your own IoT solution.
  • to learn about the most common patterns in IoT solution design and development.

Each preconfigured solution implements a common IoT scenario and is a complete, end-to-end implementation. You can deploy the Azure IoT Suite predictive maintenance preconfigured solution from https://www.azureiotsuite.com, following the guidance on provisioning pre-configured solutions outlined in this document. In addition, you can download the complete source code from this repository to customize and extend the solution to meet your specific requirements.

Predictive Maintenance pre-configured solution

The predictive maintenance pre-configured solution illustrates how you can predict the point when failure is likely to occur. The solution combines key Azure IoT Suite services, including an ML workspace complete with experiments for predicting the Remaining Useful Life (RUL) of an aircraft engine, based on a public sample data set.

Contents of this repository

Docs folder:

Other useful IoT Suite documentation:

EventProcessor folder:

  • Azure Worker Role that hosts an Event Hub EventProcessorHost instance to handle the event data from the devices forwarding event data to other back-end services or to the remote monitoring site

Visual Studio solution:

  • PredictiveMaintenance: contains the source code for the whole preconfigured solution, including the solution portal web app, the EventProcessor web job, and the Simulator web job.

Feedback

Have ideas for how we can improve Azure IoT? Give us Feedback.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

azure-iot-predictive-maintenance's People

Contributors

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