Predictive Maintenance Platform

The Challenge

BuildVision operates heavy manufacturing equipment across four production facilities. Unplanned equipment failures were costing them an average of $180K per incident in lost production, emergency repairs, and overtime labor. Their maintenance was purely reactive — fix it when it breaks.

They'd explored predictive maintenance vendors but found that generic solutions couldn't handle the variety of equipment in their facilities or integrate with their existing sensor infrastructure.

Our Approach

We connected to BuildVision's existing IoT sensor network (vibration, temperature, pressure, and power consumption sensors across 200+ machines) and built custom anomaly detection models for each equipment class.

The models learn normal operating patterns for each machine and detect degradation signatures weeks before failure. We worked with BuildVision's maintenance engineers to validate the model's predictions against their experience — the goal was augmenting their expertise, not replacing it.

The Solution

A cloud-based platform that ingests real-time sensor data via MQTT, processes it through equipment-specific ML models, and surfaces maintenance recommendations through a dashboard and mobile alerts.

The system provides remaining useful life estimates for critical components and automatically generates work orders in BuildVision's existing CMMS when confidence thresholds are met. Maintenance teams can see trend visualizations showing how equipment health is changing over time.

Results