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Solution architecture: Aircraft engine monitoring for predictive maintenance in aerospace

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 and Data Factory. 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.

Aircraft engine monitoring for predictive maintenance in aerospaceMicrosoft Azure’s Predictive Maintenance solution demonstrates how to combine real-time aircraft data with analytics to monitor aircraft health.Data Factory: Move data, orchestrate, schedule and monitorSQL DatabaseMachine LearningPower BI Event HubStream AnalyticsHDInsightGeography Data(Blob Storage)Engine Sensor Data (Simulated)

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.

HDInsight

 		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

 		Data Factory handles orchestration, scheduling and monitoring of the batch processing pipeline.

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

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