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: Azure Stream Analytics, Event Hubs, Machine Learning Studio, SQL Data Warehouse 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.

Prevención de defectos con mantenimiento predictivoVea cómo utilizar Azure Machine Learning para predecir errores antes de que ocurran con datos de ensamblado en tiempo real.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

Azure 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 visualizes real-time assembly-line data from Stream Analytics and the predicted failures and alerts from Data Warehouse.

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