Predictive Aircraft Engine Monitoring
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Microsoft Azure's Predictive Maintenance solution demonstrates how to combine real-time aircraft data with analytics to monitor aircraft health.
This solution is built on the Azure managed services: Azure Stream Analytics, Event Hubs, Machine Learning Studio, HDInsight, Azure SQL Database, Data Factory 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.
Download an SVG of this architecture.
- 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 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.
- HDInsight runs Hive scripts to provide aggregations on the raw events that were archived by Stream Analytics.
- Azure SQL Database: SQL Database stores prediction results received from Machine Learning and publishes data to Power BI.
- Data Factory handles orchestration, scheduling, and monitoring of the batch processing pipeline.
- Power BI visualizes real-time assembly-line data from Stream Analytics and the predicted failures and alerts from Data Warehouse.