I wanted to share our success story implementing automated predictive maintenance using AVEVA APM integrated with MES resource management. Over the past eight months, we’ve reduced unplanned equipment downtime from an average of 47 hours per month to just 16 hours - a 65% improvement that’s directly impacted our OEE and production capacity.
Our implementation focused on three key areas: predictive analytics using vibration and temperature sensors, seamless sensor integration with our existing MES infrastructure, and automated maintenance scheduling triggered by equipment health predictions. The integration between APM and MES resource management was critical - when APM predicts a failure, it automatically creates maintenance work orders and adjusts production schedules to minimize disruption. I’ll share our implementation approach and lessons learned.
The key is using an edge computing layer. Deploy edge gateways that collect sensor data locally, perform initial filtering and anomaly detection, then send only meaningful events to both APM (for advanced analytics) and MES (for operational context). This architecture reduces network bandwidth by 80% and provides sub-second response for critical alerts while feeding long-term trend data to APM’s machine learning models. We use MQTT protocol for sensor communication and REST APIs for MES integration.
That’s impressive results! What types of equipment are you monitoring and how many sensors did you deploy? We’re considering a similar implementation but concerned about the sensor infrastructure investment required.
We started with our most critical assets - 12 CNC machining centers, 8 injection molding machines, and 5 industrial robots. Total sensor deployment was 87 sensors: vibration sensors on all rotating equipment, temperature sensors on motors and hydraulics, and pressure sensors on pneumatic systems. Investment was about $145K for sensors and installation. We prioritized based on equipment criticality and historical failure frequency.
How did you handle the sensor data integration? Did you feed sensor data directly into APM or route it through MES first? We have a similar architecture but struggling with the data flow design - trying to avoid creating data silos while maintaining real-time responsiveness for critical alerts.
What was your biggest challenge during implementation? We attempted predictive maintenance two years ago and struggled with false positive alerts overwhelming our maintenance team. They lost confidence in the system and went back to reactive maintenance.