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Solution architecture: Defect prevention with predictive maintenance

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

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

Prévention des défaillances avec la maintenance prédictiveDécouvrez comment utiliser Azure Machine Learning pour prédire les défaillances avant qu’elles ne se produisent avec des données de ligne d’assemblage en temps réel.Azure SQL DWMachine Learning(Real time predictions)Power BIALS test measurements (Telemetry)Event HubStream Analytics(Real time analytics)Dashboard of predictions/alertsRealtime data stats, Anomaliesand aggregatesRealtime event and predictions

Implementation guidance

Products/Description Documentation

Stream Analytics

Stream Analytics provides near real-time analytics on the input stream from the Azure Event Hub. Input data is filtered and passed to a Machine Learning endpoint, finally sending the results to the Power BI dashboard.

Event Hubs

Event Hubs ingests raw assembly-line data and passes it on to Stream Analytics.

Machine Learning Studio

Machine Learning predicts potential failures based on real-time assembly-line data from Stream Analytics.

SQL Data Warehouse

SQL Data Warehouse stores assembly-line data along with failure predictions.

Power BI

Power BI visualises real-time assembly-line data from Stream Analytics and the predicted failures and alerts from Data Warehouse.

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