Operations · Azure AI Foundry
AI agent that monitors IoT sensor streams via Azure IoT Hub, detects anomaly patterns with Azure OpenAI, and triggers maintenance work orders before equipment fails — reducing downtime by up to 73%.
How It Works
We configure Azure IoT Hub ingestion, Azure Stream Analytics jobs, and Azure Digital Twins to create a live, queryable model of your asset estate.
Azure Machine Learning regression and time-series forecasting models are trained on your historical sensor data and baseline failure events, then deployed as real-time scoring endpoints.
Semantic Kernel orchestrates the full alert-to-work-order pipeline — from anomaly confirmation to CMMS record creation — with human-in-the-loop review for critical assets.
Capabilities
Continuously ingests vibration, temperature, pressure, and flow-rate telemetry from Azure IoT Hub — processing millions of events per second without data loss.
GPT-4o contextualises raw anomaly signals against asset maintenance history and operating conditions, explaining failure modes in plain language for field technicians.
When failure probability crosses configurable thresholds, the agent creates prioritised work orders directly in your CMMS or ERP — including the fault description, recommended parts, and urgency level.
Retrieves full maintenance records from Azure Data Lake and correlates current sensor patterns with past failure signatures to improve prediction confidence.
Azure Stream Analytics computes rolling anomaly scores per asset in real time, while Azure Machine Learning regression models output per-asset failure probability with confidence intervals.
Pre-built connectors for SAP PM, IBM Maximo, Infor EAM, and ServiceNow — with Logic Apps handling authentication, payload mapping, and retry logic for reliable work order delivery.
Built With