Solution architecture: Aircraft engine monitoring for predictive maintenance in aerospace

Air travel is central to modern life. However, aircraft engines are expensive and keeping them up and running requires frequent maintenance by highly skilled technicians. The production hours lost to downtime can add up, cutting into your potential profit. Plus, fuel accounts for about 10% of the total cost of running an aircraft, so efficiency matters.

Modern aircraft engines are equipped with highly sophisticated sensors that track how they are functioning. By combining the data from these sensors with advanced analytics, it is possible to both monitor the aircraft in real time and predict the remaining useful life of an engine component so that maintenance can be scheduled in a timely manner to prevent mechanical failures.

This aircraft-health-monitoring system predicts the remaining useful life of engine components. It includes data ingestion, data storage, data processing and advanced analytics—all essential for building an end-to-end predictive-maintenance solution. And while this example is customised for aircraft engine monitoring, the solution can easily be generalised for other predictive-maintenance scenarios.

By decreasing downtime and ensuring that engines are running efficiently, this solution helps you keep your fleet up and running as profitably as possible.

Deploy to Azure

Use the following pre-built template to deploy this architecture to Azure

Deploy to Azure

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Data Factory: Move data, orchestrate, schedule and monitor SQL Database Machine Learning Power BI Event Hub Stream Analytics HDInsight Geography 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 visualises real-time assembly-line data from Stream Analytics and the predicted failures and alerts from Data Warehouse.

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