ManufacturingBuilt By: FalconicLab
Prototyping Predictive Maintenance for Industrial IoT Fleets
Internal R&D on a time-series predictive maintenance model that estimates Remaining Useful Life (RUL) from streamed IoT vibration sensor data.
Hours ahead
Early Detection Window
~45%
Simulated Stoppage Reduction
The Enterprise Challenge
Unplanned equipment failure on manufacturing lines is one of the costliest and hardest problems to solve reactively — by the time a fault is audible or visible, downtime has already started.
The Falconic Engineering Solution
We prototyped an IoT edge ingestion pipeline streaming sensor telemetry into a time-series RUL estimation model, with integration patterns for SCADA and maintenance-dispatch systems.
Quantifiable Results & ROI
Demonstrated early fault detection ahead of simulated failure events in test data
Designed integration pathways for SCADA and SAP maintenance dispatch systems
Validated architecture for real-time ingestion at industrial sensor volumes
Technologies Deployed
TimescaleDBMQTT / OPC-UAPythonSAP Connector