Case Study: Global Building Equipment Manufacturer achieves rapid predictive maintenance (73% precision, 71% recall in <1 week) with C3 IoT

Predictive Maintenance for Building Equipment

Global Building Equipment Manufacturer faced costly, unexpected failures across refrigeration and HVAC assets and had no standardized, labeled sensor data to predict them. To address this, they ran a sub‑week trial of C3 Predictive Maintenance from C3 IoT to evaluate predictive maintenance for 165 chillers.

C3 IoT used the C3 Predictive Maintenance application on the C3 IoT Platform (running on AWS) to ingest unlabeled, mismatched time‑series sensor feeds via its metadata‑based C3 Type System, generate 163 time‑series analytic inputs, and train a machine‑learning classifier. In under a week (165 chillers analyzed in 4 days, with three years of data and 40–50 sensors per chiller) C3 IoT delivered a 10% model improvement in one day of tuning and achieved 73% precision and 71% recall for failure prediction.