What service can be used along with azure ml studio services to predict machine failure in real-time through data collected from sensors?
Answers
Answer:
The simulation data is streamed by a newly deployed Azure Web Job, AeroDataGenerator.
This synthetic data feeds into the Azure Event Hubs service as data points.
Two Azure Stream Analytics jobs analyze the data to provide near real-time analytics on the input stream from the event hub. One of the Stream Analytics jobs archives all raw incoming events to the Azure Storage service for later processing by the Azure Data Factory service, and the other publishes results onto a Power BI dashboard.
The HDInsight service is used to run Hive scripts (orchestrated by Azure Data Factory) to provide aggregations on the raw events that were archived by the aforementioned Stream Analytics job.
The Azure Machine Learning service is used (orchestrated by Azure Data Factory) to make predictions on the remaining useful life (RUL) of particular aircraft engine given the inputs received.
Azure SQL Database is used (managed by Azure Data Factory) to store the prediction results received from the Azure Machine Learning service. These results are then consumed in the Power BI dashboard. A stored procedure is deployed in the SQL Database and later invoked in Azure Data Factory pipeline to store the ML prediction results into the scoring result table.
Azure Data Factory handles orchestration, scheduling, and monitoring of the batch processing pipeline.
Finally, Power BI is used for results visualization, so that aircraft technicians can monitor the sensor data from an airplane or across the fleet in real time and use visualizations to schedule engine maintenance.
Answer:
Predictive maintenance service can be used along with azure ml studio services to predict machine failure in real-time through data collected from sensors
Explanation:
Predictive maintenance service also called as PdM.
This service typically discovers patterns of maintenance problem. it helps to reduce operation risks. it helps in estimation of asset lifespan.