Defect prevention with predictive maintenance using analytics and machine learning

Event Hubs
Machine Learning
Stream Analytics
Synapse Analytics
Power BI

Solution Idea

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Learn how to use Azure Machine Learning to predict failures before they happen with real-time assembly line data.

This solution is built on Azure-managed services: Azure Stream Analytics, Event Hubs, Azure Machine Learning, Azure Synapse Analytics, and Power BI. These services run in a high-availability environment, patched and supported, allowing you to focus on your solution instead of the environment they run in.

Potential use cases

Industries that benefit from this solution include:

  • Manufacturing processes
  • Airline maintenance scheduling

Architecture

Architecture diagram: defect prevention with predictive maintenance. Download an SVG of this architecture.

Components

  • Event Hubs ingests raw assembly-line data and passes it on to Azure Stream Analytics.
  • Azure Stream Analytics provides near real-time analytics on the input stream from Event Hubs. Input data is filtered and passed to an Azure Machine Learning endpoint. Results of machine learning are sent to a Power BI dashboard.
  • Azure Machine Learning predicts potential failures based on real-time assembly-line data from Stream Analytics.
  • Azure Synapse Analytics stores assembly-line data along with failure predictions.
  • Power BI enables visualization of real-time assembly-line data from Stream Analytics, and the predicted failures and alerts from Data Warehouse.

Next steps

See the product documentation:

See the following related Azure Architecture Center articles: