Multi-sensor edge IoT kits with on-device anomaly detection that transform reactive maintenance into predictive operations — deployed non-invasively on existing equipment fleets.
Industrial unplanned downtime costs manufacturers an estimated $50 billion per year globally. A single hour of downtime on a production line can cost $100K-300K depending on the sector. Yet only 12% of industrial equipment worldwide has any form of predictive monitoring — the remaining 88% operates on scheduled maintenance or, worse, run-to-failure strategies.
The problem is not awareness. Equipment manufacturers and operators know that predictive maintenance delivers 25-30% cost savings. The problem is engineering capacity. Retrofitting vibration sensors, pressure monitors, and temperature arrays onto pneumatic valves, industrial pumps, or HVAC systems requires custom PCB design, ruggedized enclosures, industrial protocol integration (IO-Link, Modbus, SPE), and edge AI firmware — none of which a typical mechanical equipment company can staff internally.
Meanwhile, competitors like Festo, Emerson, and Parker are already shipping AI-enabled condition monitoring kits with their equipment. Companies without a predictive maintenance story are losing tenders, losing customers, and losing the recurring revenue opportunity that comes with fleet-wide monitoring SaaS.
Promwad designs and delivers a complete predictive maintenance hardware and software stack — from custom sensor PCBs to cloud analytics. The system is architected for non-invasive retrofit: no changes to existing equipment firmware, no disruption to production lines, no safety recertification required.
The edge AI approach is critical. TinyML models (Random Forest, LSTM, XGBoost) run directly on the sensor node microcontroller, providing real-time anomaly detection without cloud dependency. Only aggregated health scores and anomaly alerts are transmitted upstream — reducing bandwidth requirements by 95% and enabling deployment in facilities with limited or no internet connectivity.
Yes. The system is designed for non-invasive retrofit. Vibration sensors attach magnetically or via adhesive. Pressure sensors use T-connectors on existing lines. Temperature sensors are surface-mount. No changes to equipment firmware, no production interruption, no safety recertification.
On-device TinyML models achieve 92-96% accuracy for vibration anomaly detection, compared to 95-98% for full cloud models. The trade-off is acceptable because edge inference provides real-time alerts (sub-second) without network dependency — critical for facilities with poor connectivity or safety-critical equipment where latency matters.
IO-Link, Single Pair Ethernet (SPE), Modbus RTU/TCP, CAN bus, and MQTT. The gateway supports multi-protocol aggregation, so mixed-protocol brownfield environments are supported without replacing existing infrastructure.
The client owns all data collected from their equipment and retains full IP rights to custom sensor hardware designs. Promwad retains IP on its generic TinyML framework and cloud platform components, licensed to the client under the project agreement.