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Lösungsarchitektur: 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.

In Azure bereitstellen

Verwenden Sie die folgende vorgefertigte Vorlage, um diese Architektur in Azure bereitzustellen

In Azure bereitstellen
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)

Implementierungsleitfaden

Produkte Dokumentation

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

 		HDInsight runs Hive scripts to provide aggregations on the raw events that were archived by Stream Analytics.

SQL-Datenbank

 		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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Anomaly detection with machine learning Diagram showing 10 connected icons. At the top left is the icon for time series data. Connected by a one-way arrow to the right is Event Hubs, and further to the right, connected by a one-way arrow is Stream Analytics. Continuing to the right and down, a one-way arrow leads to Azure SQL Database, which is connected by a one-way arrow to Power BI at the far right. Going back to Stream Analytics, a one-way arrow leads down to Table Storage, and Table storage is connected by a mutual arrow down to Data Factory. Data Factory is connected to four other icons. Connected by a mutual arrow to the left is Machine Learning for anomaly detection. Connected downward by a one-way arrow is Visual Studio Application Insights for monitoring and telemetry. Connected by a one-way arrow up and to the right is Azure SQL Database. That arrow is labeled “Save Machine Learning output.” Connected by a one-way arrow to the right is Service Bus topics, with publish and subscribe capabilities. That arrow is labeled “Publish anomalies detected.” Machine Learning(Anomaly Detection) Service Bus topics(Publish/subscribe capabilities) Visual Studio Application Insights(Monitoring and telemetry) Event Hub(Event queue) Table Storage(Big Data store) Stram Analytics(Realtime analytics) Metadata Save ML output Score each dataset Publish anomalies detected Power BI Azure SQL DB(Anomaly detection results) Data Factory Time series data

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