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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: 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.

Overvågning af flymotorer i forbindelse med prædiktiv vedligeholdelse i luftfartsindustrienMicrosoft Azures forudsigende vedligeholdelsesløsning viser, hvordan flydata i realtid kombineres med analyser til overvågning af et flys tilstand.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

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 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

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

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