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Every machine has a heartbeat. Most manufacturers are not listening to it.
Unplanned downtime in manufacturing costs an average of €10,000 per hour across European SMEs. Most of it is preventable. The data that would predict a bearing failure, a hydraulic pressure drop, or a motor overload exists — it is in the vibration, the temperature, the current signature of the machine. But without instrumentation and analysis, it remains invisible until the machine stops.
Your maintenance team shifts from reactive to predictive. Instead of a foreman walking the floor looking for anomalies, the machines report their own status continuously. A CNC that is developing a spindle bearing issue flags it 3 weeks before failure. The bearing is replaced during a scheduled maintenance window. Production continues. The unplanned downtime event that would have cost you a day and a half of production never happens.
Compact sensor modules — vibration (MEMS accelerometer), temperature, and motor current — retrofit to existing machines without modification to the machine itself. Sensors transmit over a local mesh network (Thread or Zigbee) to an edge gateway running a health scoring model.
The health model establishes a baseline signature for each machine during a 2–4 week learning period, then monitors for deviations. Anomalies generate alerts with a severity score and a recommended action. The system is hardware-agnostic — it works with any machine that has a motor, regardless of age or manufacturer.
No cloud dependency; the full analysis runs locally, with optional cloud sync for cross-facility benchmarking.