Architektura řešení: 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’re functioning. By combining the data from these sensors with advanced analytics, it’s 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 customized for aircraft engine monitoring, the solution can easily be generalized 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.

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Aircraft engine monitoring for predictive maintenance in aerospace Diagram showing 10 connected icons. At the top left is the icon for Engine Sensor Data from the aircraft. Connected by a one-way arrow to the right is Event Hubs. Continuing to the right by a one-way arrow is Stream Analytics. Continuing to the right and down, a one-way arrow leads to Power BI. Going back to Stream Analytics, a one-way arrow leads down to Geography Data in Azure Blob Storage. Geography Data is connected to the right and down by a one-way arrow that leads to SQL Data Warehouse. SQL Data Warehouse is connected to Power BI, above, by a one-way arrow. Geography Data is connected by a mutual arrow to HDInsight, below, which is also connected by mutual arrows to Machine Learning, to its left, and Data Factory, further below. Data Factory: Move data, orchestrate, schedule and monitor SQL Database Machine Learning Power BI Event Hub Stram Analytics HDInsight Geography Data(Blob Storage) Engine Sensor Data (Simulated)

Pokyny k implementaci

Produkty Dokumentace

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

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.

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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