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.
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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