Anomaly Detection

Identify Unusual Operating Behavior Before It Becomes Costly

Responsive Anomaly Detection at the Source

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.

Robotic Anomaly Detection
It is often undesirable to train an ML model for various fault states of interest due to safety, cost, and/or time involved in reproducing or simulating the associated sensed conditions. This is where anomaly detection becomes invaluable to characterize baseline operation and set thresholds for outliers that should be alerted. Here we see such an approach for a robotic arm motion path along with an anomaly alert indicating unintended contact with a human.
Distributed Process Monitoring

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
Platforms and Plans
SensiML offers complete Knowledge Pack development services allowing you to focus on your application. Our team can devise a customized project plan using your baseline normal operation and available anomaly training data to create a tailored recognition libraries ready to drop into your application.
Platforms and Plans
For project teams with basic machine learning familiarity, desire to learn, or needing to undertake the tasks entirely in-house, SensiML offers its ML Analytics Toolkit suite. A true end-to-end workflow, SensiML Analytics Toolkit supports the complete process from data collection and labeling to Knowledge Pack generation and testing.