Edge Spectrum develops machine learning models for resource-constrained devices such as sensors.
Training and inference are performed directly on the sensors (or nearby when reusing existing sensors), processing raw data on the fly.

The benefits of this approach are numerous:

• It adapts to all types of sensors (vibration, pressure, temperature, electrical consumption, etc.).
• Alerts and safeguard actions can be triggered and executed in real time.
• Bandwidth usage is minimized, as only equipment health status is transmitted.
• Tolerance to network and cloud outages: sensors continue operating autonomously even without connectivity.
• Increased adaptability to changing environments: learning remains available on the sensors at any time, allowing models to be updated locally when needed.

Applications include:

• Predictive maintenance and monitoring of critical equipment and infrastructure,
• Monitoring of production parameters,
• Self-diagnosis of the sensors themselves.