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 and Azure Synapse Analytics. 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.

使用預測性維護預防瑕疵了解如何使用 Azure Machine Learning 在即時生產線資料發生失敗之前,先預測到失敗。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.

Azure Synapse Analytics

Synapse Analytics stores assembly-line data along with failure predictions.

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

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