使用預測性維護預防瑕疵

事件中樞
Machine Learning
串流分析
Synapse Analytics

解決方案構想 Solution Idea

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瞭解如何使用 Azure Machine Learning,在發生即時程式列資料之前預測失敗。Learn how to use Azure Machine Learning to predict failures before they happen with real-time assembly line data.

此解決方案建基於 Azure 受控服務: Azure 串流分析事件中樞Azure Machine LearningAzure Synapse AnalyticsPower BIThis solution is built on the Azure managed services: Azure Stream Analytics, Event Hubs, Azure Machine Learning, Azure Synapse Analytics and Power BI. 這些服務是在高可用性環境中執行(經過修補和支援),可讓您專注于解決方案,而不是其執行所在的環境。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.

架構Architecture

架構圖表會 下載此架構的SVGArchitecture Diagram Download an SVG of this architecture.

元件Components

  • Azure 串流分析:串流分析可針對來自 Azure 事件中樞的輸入資料流程提供近乎即時的分析。Azure Stream Analytics: Stream Analytics provides near real-time analytics on the input stream from the Azure Event Hub. 輸入資料會經過篩選並傳遞至 Machine Learning 端點,最後將結果傳送至 Power BI 儀表板。Input data is filtered and passed to a Machine Learning endpoint, finally sending the results to the Power BI dashboard.
  • 事件中樞 會內嵌原始的元件行資料,並將其傳遞至串流分析。Event Hubs ingests raw assembly-line data and passes it on to Stream Analytics.
  • Azure Machine Learning: Machine Learning 根據串流分析中的即時元件行資料來預測潛在的失敗。Azure Machine Learning: Machine Learning predicts potential failures based on real-time assembly-line data from Stream Analytics.
  • Azure Synapse Analytics: Synapse Analytics 會儲存元件行資料及失敗預測。Azure Synapse Analytics: Synapse Analytics stores assembly-line data along with failure predictions.
  • Power BI 從串流分析將即時的元件行資料視覺化,以及從資料倉儲預測的失敗和警示。Power BI visualizes real-time assembly-line data from Stream Analytics and the predicted failures and alerts from Data Warehouse.

後續步驟Next steps