Continuous Improvement with Collaborative AI
Efficient Azure Image Workflow
The images taken by the drones are uploaded into Sharepoint and transferred into Azure Data Lake Gen2. Azure Logic Apps assigns the pictures to each power line and pole. Subsequently, the images undergo refinement via Azure Functions and are imported into Cloud-based SaaS Grid Vision® through Azure Event Hubs as instances.
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Various Azure services are involved, with Azure Kubernetes Service, Azure Database for PostgreSQL, and Cosmos DB playing significant roles. Next, AI examines the images, identifying potential problems like component issues or power pole damage. An expert user subsequently validates the AI's conclusions before exporting the results to the Cloud, where they are transformed into a checklist.
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The gathered data enhances the inspections of distribution system operators, enabling the transition from periodic to predictive maintenance through identifying defect patterns and making predictions more accurate. Grid Vision®, a widely used utility software, employs AI models supported by expert user input to continually train and improve the AI through a global feedback loop, termed collaborative AI.
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