What do each of these things have in common?
- Sensing imminent damage to high-value machinery
- Isolating outlier product on high-speed processing lines
- Unusual security sensor conditions requiring a closer look
The answer is none of them can afford the time necessary to stream data to the cloud for remote ML processing and anomaly detection.
These examples and many others require the speed of real-time anomaly detection that takes place at the IoT endpoint where the monitored event originates. SensiML specializes in developing customized anomaly detection algorithms that can run in the limited computing footprint available for execution at the sensor endpoint node for the ultimate in low-latency anomaly detection.
The desire for factory-wide consistent monitoring is often frustrated by the sheer variety of new and old equipment, controls, and networks involved. With edge AI sensing, a solution now exists. With decentralized, trainable ML sensor networks factory managers can:
- Add over-the-top monitoring endpoints without impacting existing systems
- Deploy smart ML sensors that learn and adapt with fast and simple training
- Standardize monitoring consistently across new and legacy machines
