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솔루션 아키텍처: 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.

이 솔루션은 Azure 관리 서비스를 기반으로 합니다. Stream Analytics, Event Hubs, Machine Learning Studio, HDInsight, Azure SQL Database, Data Factory 및 Power BI. 이러한 서비스는 고가용성 환경에서 실행되고 패치되며 지원되므로, 솔루션이 실행되는 환경 대신 솔루션에 집중할 수 있습니다.

Aircraft engine monitoring for predictive maintenance in aerospaceMicrosoft Azure’s Predictive Maintenance solution demonstrates how to combine real-time aircraft data with analytics to monitor aircraft health.Data Factory: Move data, orchestrate, schedule and monitorSQL DatabaseMachine LearningPower BI Event HubStream AnalyticsHDInsightGeography Data(Blob Storage)Engine Sensor Data (Simulated)

구현 가이드

제품/설명 설명서

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

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

관련 솔루션 아키텍처

Defect prevention with predictive maintenanceLearn how to use Azure Machine Learning to predict failures before they happen with real-time assembly line data.Azure SQL DWMachine Learning(Real time predictions)Power BIALS test measurements (Telemetry)Event HubStream Analytics(Real time analytics)Dashboard of predictions/alertsRealtime data stats, Anomaliesand aggregatesRealtime event and predictions

예측 유지 관리를 통해 결함 방지

Azure Machine Learning을 사용하여 실시간 어셈블리 라인 데이터에서 오류가 발생하기 전에 미리 예측하는 방법을 알아봅니다.

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Predictive insights with vehicle telematicsLearn how car dealerships, manufacturers, and insurance companies can use Microsoft Azure to gain predictive insights on vehicle health and driving habits.Data Factory: Move data, orchestrate, schedule and monitorSQL Data WarehouseMachine LearningMachine LearningPower BI Event HubStream AnalyticsHDInsightGeography Data(Blob Storage)Vehicle CatalogueDiagnotic Events (Simulated)

자동차 텔레매틱스를 통한 예측 정보

자동차 딜러, 제조업체, 보험사가 어떻게 Microsoft Azure를 사용하여 차량 상태와 운전 습관에 대한 예측적 정보를 얻을 수 있는지 알아보세요.

자세히 보기
Anomaly detection with machine learningMicrosoft Azure’s IT Anomaly Insights can help automate and scale anomaly detection for IT departments to quickly detect and fix issues.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)Stream Analytics(Realtime analytics)MetadataSave ML outputScore each datasetPublish anomalies detectedPower BI Azure SQL DB(Anomaly detection results)Data FactoryTime series data

기계 학습을 통한 이상 감지

Microsoft Azure의 IT Anomaly Insights는 문제를 빠르게 감지하고 해결할 수 있도록 이상 감지를 자동화 및 확장하도록 돕습니다.

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